{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n",
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('y_train: ', (49000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984946819e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.39907127746e-11\n",
      "dw error:  1.088958711e-10\n",
      "db error:  2.41228675681e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27563491363e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  1.1411466627e-10\n",
      "dw error:  8.16201557044e-11\n",
      "db error:  7.82672402146e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.9996027491\n",
      "dx error:  1.40215660067e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.3025458445\n",
      "dx error:  9.38467316199e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.48e-10\n",
      "b1 relative error: 6.55e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 3.12e-07\n",
      "W2 relative error: 7.98e-08\n",
      "b1 relative error: 1.35e-08\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.304060\n",
      "(Epoch 0 / 10) train acc: 0.116000; val_acc: 0.094000\n",
      "(Iteration 101 / 4900) loss: 1.829613\n",
      "(Iteration 201 / 4900) loss: 1.857390\n",
      "(Iteration 301 / 4900) loss: 1.744448\n",
      "(Iteration 401 / 4900) loss: 1.420187\n",
      "(Epoch 1 / 10) train acc: 0.407000; val_acc: 0.422000\n",
      "(Iteration 501 / 4900) loss: 1.565913\n",
      "(Iteration 601 / 4900) loss: 1.700510\n",
      "(Iteration 701 / 4900) loss: 1.732213\n",
      "(Iteration 801 / 4900) loss: 1.688361\n",
      "(Iteration 901 / 4900) loss: 1.439529\n",
      "(Epoch 2 / 10) train acc: 0.497000; val_acc: 0.468000\n",
      "(Iteration 1001 / 4900) loss: 1.385772\n",
      "(Iteration 1101 / 4900) loss: 1.278401\n",
      "(Iteration 1201 / 4900) loss: 1.641580\n",
      "(Iteration 1301 / 4900) loss: 1.438847\n",
      "(Iteration 1401 / 4900) loss: 1.172536\n",
      "(Epoch 3 / 10) train acc: 0.490000; val_acc: 0.466000\n",
      "(Iteration 1501 / 4900) loss: 1.346286\n",
      "(Iteration 1601 / 4900) loss: 1.268492\n",
      "(Iteration 1701 / 4900) loss: 1.318215\n",
      "(Iteration 1801 / 4900) loss: 1.395750\n",
      "(Iteration 1901 / 4900) loss: 1.338233\n",
      "(Epoch 4 / 10) train acc: 0.532000; val_acc: 0.497000\n",
      "(Iteration 2001 / 4900) loss: 1.343165\n",
      "(Iteration 2101 / 4900) loss: 1.393173\n",
      "(Iteration 2201 / 4900) loss: 1.276734\n",
      "(Iteration 2301 / 4900) loss: 1.287951\n",
      "(Iteration 2401 / 4900) loss: 1.352778\n",
      "(Epoch 5 / 10) train acc: 0.525000; val_acc: 0.475000\n",
      "(Iteration 2501 / 4900) loss: 1.390234\n",
      "(Iteration 2601 / 4900) loss: 1.276361\n",
      "(Iteration 2701 / 4900) loss: 1.111768\n",
      "(Iteration 2801 / 4900) loss: 1.271688\n",
      "(Iteration 2901 / 4900) loss: 1.272039\n",
      "(Epoch 6 / 10) train acc: 0.546000; val_acc: 0.509000\n",
      "(Iteration 3001 / 4900) loss: 1.304489\n",
      "(Iteration 3101 / 4900) loss: 1.346667\n",
      "(Iteration 3201 / 4900) loss: 1.325510\n",
      "(Iteration 3301 / 4900) loss: 1.392728\n",
      "(Iteration 3401 / 4900) loss: 1.402001\n",
      "(Epoch 7 / 10) train acc: 0.567000; val_acc: 0.505000\n",
      "(Iteration 3501 / 4900) loss: 1.319024\n",
      "(Iteration 3601 / 4900) loss: 1.153287\n",
      "(Iteration 3701 / 4900) loss: 1.180922\n",
      "(Iteration 3801 / 4900) loss: 1.093164\n",
      "(Iteration 3901 / 4900) loss: 1.135902\n",
      "(Epoch 8 / 10) train acc: 0.568000; val_acc: 0.490000\n",
      "(Iteration 4001 / 4900) loss: 1.191735\n",
      "(Iteration 4101 / 4900) loss: 1.359396\n",
      "(Iteration 4201 / 4900) loss: 1.227283\n",
      "(Iteration 4301 / 4900) loss: 1.024113\n",
      "(Iteration 4401 / 4900) loss: 1.327583\n",
      "(Epoch 9 / 10) train acc: 0.592000; val_acc: 0.504000\n",
      "(Iteration 4501 / 4900) loss: 0.963330\n",
      "(Iteration 4601 / 4900) loss: 1.445619\n",
      "(Iteration 4701 / 4900) loss: 1.007542\n",
      "(Iteration 4801 / 4900) loss: 1.005175\n",
      "(Epoch 10 / 10) train acc: 0.611000; val_acc: 0.512000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "solver = Solver(model,\n",
    "                data,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                    'learning_rate':1e-3,\n",
    "                },\n",
    "                lr_decay=0.95,\n",
    "                num_epochs =10,batch_size =100,\n",
    "                print_every = 100)\n",
    "\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BHTveA5wLXEfqWZYHDgd+C6yrObsF+BYpKDuL1NvsDuAhXn75BqZO/RiFQqHP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\n7fTaYKAM2RlBdKMo3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JRjjouCs2SQcO/W6qvvyXe55q8h7MdvoCgmXF2oarC6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\nIzx69EhYY3rvA9Rqf8GpU3OHB52azTTIOtiwSyCAmswQrv3paYj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egdJqNRIo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l5+Rpd97C84B5LnRyjgfKMODGwL/FxarvFC8n4Hxfy449u2Rkt3gEwKToWPXy6XC9QCtIE8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\nmAuJey7Ohlnjbc/Fp2CKJLrPORdAO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PSzSXbw0sHW/Yj79O8dC2VN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\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HP3w0vU/oza92+1XSwSRJkiRJklQGZXRZVqfONbRseQYDBjzFxRMvZu/qe3P9MdcnHUuS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\nTTqGpHz4MyplLn8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f804f4978d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.30047908977\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.05211477653\n",
      "W1 relative error: 3.90e-09\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 2.13e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.57e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 9.817983\n",
      "(Epoch 0 / 20) train acc: 0.140000; val_acc: 0.120000\n",
      "(Epoch 1 / 20) train acc: 0.220000; val_acc: 0.127000\n",
      "(Epoch 2 / 20) train acc: 0.520000; val_acc: 0.141000\n",
      "(Epoch 3 / 20) train acc: 0.660000; val_acc: 0.157000\n",
      "(Epoch 4 / 20) train acc: 0.700000; val_acc: 0.154000\n",
      "(Epoch 5 / 20) train acc: 0.860000; val_acc: 0.176000\n",
      "(Iteration 11 / 40) loss: 0.313150\n",
      "(Epoch 6 / 20) train acc: 0.880000; val_acc: 0.173000\n",
      "(Epoch 7 / 20) train acc: 0.880000; val_acc: 0.172000\n",
      "(Epoch 8 / 20) train acc: 0.940000; val_acc: 0.178000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.171000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.163000\n",
      "(Iteration 21 / 40) loss: 0.245402\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.172000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.171000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.168000\n",
      "(Iteration 31 / 40) loss: 0.071556\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.173000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.168000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.171000\n"
     ]
    },
    {
     "data": {
      "image/png": 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FAADQSYACAADoJEABAAB0EqAAAAA6CVAAAACdBCgAAIBOAhQAAEAnAQoAAKCTAAUAANBJ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\nJZn5dnFtVT3Q/mwejNCm+qDNEfNghJJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f804f4b8748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "# weight_scale = 1e-2\n",
    "# learning_rate = 1e-4\n",
    "weight_scale = 3e-2\n",
    "learning_rate = 1e-3\n",
    "\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 5.557032\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.114000\n",
      "(Epoch 1 / 20) train acc: 0.240000; val_acc: 0.086000\n",
      "(Epoch 2 / 20) train acc: 0.340000; val_acc: 0.134000\n",
      "(Epoch 3 / 20) train acc: 0.560000; val_acc: 0.128000\n",
      "(Epoch 4 / 20) train acc: 0.700000; val_acc: 0.132000\n",
      "(Epoch 5 / 20) train acc: 0.800000; val_acc: 0.122000\n",
      "(Iteration 11 / 40) loss: 1.018360\n",
      "(Epoch 6 / 20) train acc: 0.840000; val_acc: 0.137000\n",
      "(Epoch 7 / 20) train acc: 0.880000; val_acc: 0.151000\n",
      "(Epoch 8 / 20) train acc: 0.920000; val_acc: 0.138000\n",
      "(Epoch 9 / 20) train acc: 0.940000; val_acc: 0.136000\n",
      "(Epoch 10 / 20) train acc: 0.940000; val_acc: 0.154000\n",
      "(Iteration 21 / 40) loss: 0.398928\n",
      "(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.146000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.147000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.138000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.138000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.131000\n",
      "(Iteration 31 / 40) loss: 0.237235\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.143000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.139000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.137000\n"
     ]
    },
    {
     "data": {
      "image/png": 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hVVcKTwAAwLAQjlZo584bMjZ2csF9Y2P3Zteup69xRQAAwHIIRyt05MitmZi4M2Nj9+RS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\nX6zL4bsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f801c0b8ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "# learning_rate = 1e-3\n",
    "# weight_scale = 1e-5\n",
    "learning_rate = 6e-3\n",
    "weight_scale = 5e-2\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.559978\n",
      "(Epoch 0 / 5) train acc: 0.103000; val_acc: 0.108000\n",
      "(Iteration 11 / 200) loss: 2.291086\n",
      "(Iteration 21 / 200) loss: 2.153591\n",
      "(Iteration 31 / 200) loss: 2.082693\n",
      "(Epoch 1 / 5) train acc: 0.277000; val_acc: 0.242000\n",
      "(Iteration 41 / 200) loss: 2.004171\n",
      "(Iteration 51 / 200) loss: 2.010409\n",
      "(Iteration 61 / 200) loss: 2.024528\n",
      "(Iteration 71 / 200) loss: 2.024759\n",
      "(Epoch 2 / 5) train acc: 0.351000; val_acc: 0.307000\n",
      "(Iteration 81 / 200) loss: 1.804447\n",
      "(Iteration 91 / 200) loss: 1.915225\n",
      "(Iteration 101 / 200) loss: 1.917282\n",
      "(Iteration 111 / 200) loss: 1.705442\n",
      "(Epoch 3 / 5) train acc: 0.406000; val_acc: 0.316000\n",
      "(Iteration 121 / 200) loss: 1.700119\n",
      "(Iteration 131 / 200) loss: 1.772107\n",
      "(Iteration 141 / 200) loss: 1.787144\n",
      "(Iteration 151 / 200) loss: 1.822527\n",
      "(Epoch 4 / 5) train acc: 0.430000; val_acc: 0.320000\n",
      "(Iteration 161 / 200) loss: 1.623262\n",
      "(Iteration 171 / 200) loss: 1.896449\n",
      "(Iteration 181 / 200) loss: 1.544795\n",
      "(Iteration 191 / 200) loss: 1.707001\n",
      "(Epoch 5 / 5) train acc: 0.450000; val_acc: 0.323000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 3.153778\n",
      "(Epoch 0 / 5) train acc: 0.105000; val_acc: 0.093000\n",
      "(Iteration 11 / 200) loss: 2.145874\n",
      "(Iteration 21 / 200) loss: 2.032563\n",
      "(Iteration 31 / 200) loss: 1.985848\n",
      "(Epoch 1 / 5) train acc: 0.311000; val_acc: 0.281000\n",
      "(Iteration 41 / 200) loss: 1.882354\n",
      "(Iteration 51 / 200) loss: 1.855372\n",
      "(Iteration 61 / 200) loss: 1.649133\n",
      "(Iteration 71 / 200) loss: 1.806432\n",
      "(Epoch 2 / 5) train acc: 0.415000; val_acc: 0.324000\n",
      "(Iteration 81 / 200) loss: 1.907840\n",
      "(Iteration 91 / 200) loss: 1.510681\n",
      "(Iteration 101 / 200) loss: 1.546872\n",
      "(Iteration 111 / 200) loss: 1.512047\n",
      "(Epoch 3 / 5) train acc: 0.434000; val_acc: 0.321000\n",
      "(Iteration 121 / 200) loss: 1.677301\n",
      "(Iteration 131 / 200) loss: 1.504686\n",
      "(Iteration 141 / 200) loss: 1.633253\n",
      "(Iteration 151 / 200) loss: 1.745081\n",
      "(Epoch 4 / 5) train acc: 0.460000; val_acc: 0.353000\n",
      "(Iteration 161 / 200) loss: 1.485411\n",
      "(Iteration 171 / 200) loss: 1.610416\n",
      "(Iteration 181 / 200) loss: 1.528331\n",
      "(Iteration 191 / 200) loss: 1.449158\n",
      "(Epoch 5 / 5) train acc: 0.507000; val_acc: 0.384000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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EY1RWnrGXK5IkSZIkSdJIMuwDtGuv/RQVFXeRSCxlz0y0SCKxlIqKu6mpuWEw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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8014193f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  0.207207036686\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 3.476928\n",
      "(Epoch 0 / 5) train acc: 0.105000; val_acc: 0.100000\n",
      "(Iteration 11 / 200) loss: 2.036673\n",
      "(Iteration 21 / 200) loss: 2.231719\n",
      "(Iteration 31 / 200) loss: 1.986361\n",
      "(Epoch 1 / 5) train acc: 0.290000; val_acc: 0.234000\n",
      "(Iteration 41 / 200) loss: 1.965302\n",
      "(Iteration 51 / 200) loss: 1.855777\n",
      "(Iteration 61 / 200) loss: 1.984887\n",
      "(Iteration 71 / 200) loss: 1.694788\n",
      "(Epoch 2 / 5) train acc: 0.321000; val_acc: 0.295000\n",
      "(Iteration 81 / 200) loss: 1.724264\n",
      "(Iteration 91 / 200) loss: 1.675686\n",
      "(Iteration 101 / 200) loss: 1.686885\n",
      "(Iteration 111 / 200) loss: 1.922629\n",
      "(Epoch 3 / 5) train acc: 0.338000; val_acc: 0.316000\n",
      "(Iteration 121 / 200) loss: 1.611165\n",
      "(Iteration 131 / 200) loss: 1.840265\n",
      "(Iteration 141 / 200) loss: 1.741776\n",
      "(Iteration 151 / 200) loss: 1.587148\n",
      "(Epoch 4 / 5) train acc: 0.419000; val_acc: 0.359000\n",
      "(Iteration 161 / 200) loss: 1.824959\n",
      "(Iteration 171 / 200) loss: 1.495008\n",
      "(Iteration 181 / 200) loss: 1.643698\n",
      "(Iteration 191 / 200) loss: 1.522285\n",
      "(Epoch 5 / 5) train acc: 0.421000; val_acc: 0.362000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.589166\n",
      "(Epoch 0 / 5) train acc: 0.119000; val_acc: 0.146000\n",
      "(Iteration 11 / 200) loss: 2.032921\n",
      "(Iteration 21 / 200) loss: 1.897278\n",
      "(Iteration 31 / 200) loss: 1.770793\n",
      "(Epoch 1 / 5) train acc: 0.381000; val_acc: 0.320000\n",
      "(Iteration 41 / 200) loss: 1.895731\n",
      "(Iteration 51 / 200) loss: 1.681091\n",
      "(Iteration 61 / 200) loss: 1.487204\n",
      "(Iteration 71 / 200) loss: 1.629973\n",
      "(Epoch 2 / 5) train acc: 0.429000; val_acc: 0.350000\n",
      "(Iteration 81 / 200) loss: 1.506686\n",
      "(Iteration 91 / 200) loss: 1.610741\n",
      "(Iteration 101 / 200) loss: 1.486124\n",
      "(Iteration 111 / 200) loss: 1.559454\n",
      "(Epoch 3 / 5) train acc: 0.492000; val_acc: 0.359000\n",
      "(Iteration 121 / 200) loss: 1.496860\n",
      "(Iteration 131 / 200) loss: 1.531552\n",
      "(Iteration 141 / 200) loss: 1.550195\n",
      "(Iteration 151 / 200) loss: 1.657838\n",
      "(Epoch 4 / 5) train acc: 0.533000; val_acc: 0.354000\n",
      "(Iteration 161 / 200) loss: 1.603105\n",
      "(Iteration 171 / 200) loss: 1.405372\n",
      "(Iteration 181 / 200) loss: 1.503740\n",
      "(Iteration 191 / 200) loss: 1.385278\n",
      "(Epoch 5 / 5) train acc: 0.531000; val_acc: 0.374000\n",
      "\n"
     ]
    },
    {
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zd69OLS3VO507a392tvyDBoWMKBvw2msalpGhNzdsCB0N\nd+edIf1oyMgnr9ernNwcLV+zXD7Hpxh/jBK37dbWPd6a+woEriNptNutt3bsOOxrNYTX61X66HR5\n+nvk7+cP7sjgbHeU8lmKilY332i41tA/AAAAAAAQsHXrVqWlpUlSmrV2a1O332ancM6evbAyPBtb\nrdTI2v9RefnDCuyhebDc7x8rj8cqJ2eR8vPnBkqr73JZfffMuqZkSlKnTopNSFD//fvVae9e/dih\ngwZee62euuMOLSgoqD1t9M03DxmUNXTaoMvlUv6CfOUrP7hZwEVJSTJ7wo+eMpLifL5g3WhPV5x9\n3+xAONXfH9IJfz+/PNajnNwc5S/Ij2ofWnP/AAAAAADAkdFmR6AlJ49SSclbUq2xVqMkhSuXJCu3\ne7R27HgrWJI9a5YKEhMDI8eqLF4snXZa7Z01JTmbNimrtFT5eXl1BmJNsZZWQ9sYlZyst0pKwt79\nHkmn9eyp9snJ8nXsqJjycmUOHaq83/++QSOtDtXH5NRklUwoqeutkLvQrR3FR2Y0XDgtvX8AAAAA\nACCAEWgNYK2Vzxev2smHlRSuvIqRzxcXMgIr7C6Xl18uzZwpVVSE7MJZc5H/usKjusoPFTh5vV7N\nnjdPhevWNTjgysjM1KqCAo31+0PKvZJO79ZN/5udHdjUoPKeCjZv1trx48OuvdaYPlpr5Wvni/RW\nyOf4mm3h/pbePwAAAAAAcOS0yQDNGKOYmDIFArPq4YaRFK68yh7t2bNDJ554vny+eMXElCkzM0Or\nly6tNf1y7AUXSN9/rzfrsZNnJF6vVwtnz9Z7hYWK9/lUFhOjjMxM3ZaXF9KO1+tV+vjx8lx4ofy5\nuQ0OuG7Ly9PEtWtlPR6N9fuDu3BOTkgI7DZaYwdR/6BB8kjKmT9f+XWt+9aAPhpjFFMRU/dbYaWY\niphmC6daev/qQqAHAAAAAEDTa5MBmiRlZmaooGBVjTXQJClD0kpJ42qUeyWNkdc7X17vBapaMb6g\nYJXWrp2ioqJldU7LbMwi/xPT0zXd49HcamHWqoICTVy7VsuKDi5SP3vevEAwVX3aaFXAZe0hA66q\nPrpcLi0rKtKinBw9uHy54nw+7YuJ0ebY2Nq7jVbyDxyo5XPm6FCrfUXso2qHcJmjMlWwvSCwQH8N\nzueOJpw/4RBXrL+GvEdHsn+N4fV6Nfu+2SpcUyhfO59iKmKUOSpTeXPy2OQAAAAAAIAm4DR3B6Il\nL+82paQ8KMdZqUAsJQV24TxdHTveKsd5I6RcypKUo0CwVhW0VG0uME05OYsCJU2wyH/VFNGFs2dr\nerWRYKq88li/X9M8Hi3KyQme89rbbwemkIbhHzRIr739dkjbUiBYyZ41S8kZGUoaOVLJGRnKnjVL\nkjQ3P19v7dihV7/8Uqu3b1dCz57hN0UI3KB8HTqorvXyqsoL162ru48DB2r5unUhZXlz/h977x4f\nRX3v/z9nwoaQZBM0UpWIZlGBKNXzBQVCaLEVuSfYcqw/W2219aG0xlgBQUkQjiYUq6CprB6krZ5W\n22NPqZIFAblU5JIL4jkqNkCRREMCFiJsNhtIlsz8/pi9ze7MXpINoH6e/yib2c98PrMzuzuvfb3f\nr3Jy/5mLfFDWvRTyQZncg7mUlZYZzycCsazf5TIOUQilN+aXaHxJofYjdhoKG2ia3kRDYQP2o3by\nJubFvFaBQCAQCAQCgUAgEAgE5nxlHWhWq5WqqtWUli6jsnI5Hk8qFks7hYX5zJ//Dk899RKVlc/6\nH29pOYHLFepK01CUyVRWLqeiB4GLRqWaJ1taWKyEu5tAE9GWV1ZChZageaKjI6LAdaS1lZz/l8OZ\nPmewdFmY9O1JvLvnY/YXFEQsp/SJf2Fpo8GoKpaODp1QGNrrrM/p0xyLMscOi0XnBLNarVS9XUVp\nWSmVjko8sgeLYqFwQiFlL5TF7J4y6rs2acwY3q2tjbr+SCRqfr3J2UwK7e3yUFF+KhAIBAKBQCAQ\nCASC85WvbApnKJESMQEGDbqVpqY1ps/Pzp5BY+ObPS7VnOR1mynAFGBjhOfNyM7mzcZGJEmi3xVX\ncPqVV0wFrj533cmZe5vx14GuTYeZCyAvL2zz4KRQH4Zpoybb63qd+YIVVBUefBCef950jpm/+AUn\n6+pM19ud1FLTuSxdCjfdFPP6Y6E3U1W7S1hSaHDPtgQkhSYiuCLq+KL8VCAQCAQCgUAgEAgEPaS3\nUzi/siWcoURKxNSHDhihYrG4uy2SGJVqykBXxD2C26I1qVdVlUs7OpCrqw23lauquKDdGXhAAk5l\nRO5pFlpO+dhj5K5bh1xbqwlQoCWL1tZqyaKPPurfVtfrzHdMJAlGjoSaGtM5Zjid3mGji7Yul4vi\necXYRtgYNGoQthE2iucV+0sSfWOYzuX48bjWHwuhDryelIcmAn9SaCewPQ3+MhDWDdH+uz0NOgNJ\noe/wIo8AACAASURBVN3BJ07a+/enoayMpscfp6GsDHv//uRNn97jtYryU4FAIBAIBAKBQCAQfFn4\nypZwxot56ADI8gYKC8d1e+ydDodhqWY+sAHNiRbKBllmXGGgSf3lffuSumwZdbNno+Tl+Z1WclUV\nucuX41bcHAt2IaWmx9zTzBcuULV2LaVLl+rSRkOTRRVF0XqdlRn0/7r9dnj8cVAUzfkVNMchy5Zx\nqk8fbPn5OifTo0VFLF2xQl9+OXYs27at5cDQAyiFit9Vt2L/Cl4bsYb0iwbS1a8flo4OWk6cQLHb\n9fNQVUhJiWn93XUUJiIRtadIkkRSZxK8kQV3z4FRYwIOvJpq+K9lJCUndVv4jTcUIu7xz2L5qUAg\nEAgEAoFAIBAIBD1BCGheysvnsnXrTOrqVK+Ipqk2sryB3NxnKStb3a1xVVUlzePBSMKYC8xEc6JN\nI1B9uUGWeXrIEEZ3dDDBZiPN4+GL1lYWuVzsWLKEysxMPOnpWNraKHQ6Get2c//ooIEloL3NvKeZ\n203r4cMMHjcurCyvorycCvRlic3NzUy/aRyf7/0Yq6LQOPhq43FTU+GJJ0i7/34G2O3+OU4+eZJt\n/frx6S9/qbnCvCLPih07eGnMGDxFRToh6qWaGtSWYzBICZQjdoL6wYV88bNZfDHaO4aiwPz54XOR\nJDh9Oq6ebvHQ28JSPPTvfxmfTZkOo4NKVSUJxuSBOpsLNr4V8fmRRERToZTYk1kj4djs0ARSo/Gv\nVKh0VFLRoz0IBAKBQCAQCAQCgUCQGISA5iVS6EBZ2epuO4okScJtsehaU/n3CfwVGGe1UpGVRarH\nQ7vFwg2TJyNv28b4VatY4i37bAUmASVuN8+53f4x1gL3DwDnd0MGv9SpuZDGhPQAa2+HefNw3XMP\nrmAxq6aGzVOnUvPWW7pwgebmZvKvGsyKUx3+fNL+bW04zcSpfv0YoCjUNzf711yclsa+OXP0/cgk\nCfXgQToeeABChCh1zBhQZ8OWJfAt71pr0+Anc/RCkSxrIprRXIYPh9paGD2aUOTduym86abwucdI\nbwtL8XCyU4HRxqWqjMnjpGNt2MOx9DVTVRVP37694uLzOR89SZ7wi8I/fqD8VAQLCAQCgUAgEAgE\nAoHgXPO16YEWC1arlYqKxdTXb6Kx8U3q6zdRUbG4x+V4+QUFbJCND/UOWeb799zDpvp63vjsMzbV\n15NssTB3/35dz7QM4G00we1Gq5Vbs7OZmJPDshHXcfRmCfqGDDzKDauWIVVV6XqaUVEBd94ZKLEE\nv2hVN3Uq8558UjfMHQVTWHGqw++QA7A6nab92Kiq4ptOp188U4G/ZGZqolgoe/fqxbNgxuTB0czA\nv49kaiWKofiEslBuvx3+8Iew9Rv1dIsHVVXpSE6OKW20t1FVla5+/SLO5UxKim4usfY1kyQpkMxq\nvPO4XHyhPe0GjxxM6/HWiE0ALV0WIZ4JBAKBQCAQCAQCgeC8QDjQTEjEjbvPPXP/o4+S/9Lv+U3H\nKaajBIIykSnu04/vnHFhG2HzpxD2/6TFsGeaFXgZmJiVxZuHDiFJkr8Re12fOpQrA/3C5EaZIVkD\nuOnECTYE9TRrOtKMx0w8GjOG1x74BS/++tf+hz7f+zFTg9cEXOd2k2nSj23o8uV8qqpMzMkh1ePB\n3acP7RdeGC7yxNCnjJT0gMBi1tPt9tth0SLNiRbkqJP37mVI//5h6w/t6RYvkiTRfuRIxPLQ9qNH\nz4rwoxO5YixVjaf8tGD8eOy7dxsnswa5+KK5xPzn6FV1up52rAH+CQwJf478iUzhLYXhfxAIBAKB\nQCAQCAQCgeAcIAS0BONyuSh5sgTHZodfEMuwDKChYyV3UctFVJKJBycWjjMZZ/I6Xna+DIVowoIC\nI5+OWNlGqsfj/7fVaqXq7SpKy0qpdFTikT1YFAuFEwope6FMV5IHYPnmtRFFq3ZZ8m+rqioZqqqb\niwR0ALtaWlho0I/tSbeb7+fksKm+3i+s2PLzcYWKPDH0KeNUW+BAmPV0S02FxYuxPvwwWevW6YWy\nDRt060+UqGVtbcVVXa2JhyEEp42eDWIVuXzEU35a/thjbJ0+nTrv3/zi5O7dDFmzho6xY8NCIYLL\nQH2YhQUwBfgjSEioV6sB8fcTmdyDuZS9YDxPgUAgEAgEAoFAIBAIzjZCQEsgpk6bfzbAP36Fs60K\np9aiH5CgbzEUNsHVQYPIcKIvqKeMRTQVcFv0pW1Wq5WKpyqooCK6UOSOEC6gqiitrQweOdgv/vVR\n1LD+bfnALqDC7abC7db9fX1QeqhvHqYiz/DhUFOjOcdCqa6Gfq3+Q8UlJj3dAPnjj7nnttuoKC83\nXX+ixDNVVbkOTB14ucuXc0UPeoPF+5xIIlfuunWUrQ30QIu3r5lZMuvkMWPYJsusGjAgphRS07CA\nvsCdkP5aOln7skzFX4FAIBAIBAKBQCAQCM41QkBLIKZOmyFAQR38rRQ6KvDLTakOuDpcWGgZAut2\nw3SD/lAbggQqI4IFGJfLxTMlJex0OEjzeHBbLPRra6PNRIiiqgr1GydpmO72i3+ZjbDuOEwP2iw4\nPdQXLOBLD302N5fVXoeTT4gxE3mkq64i+YUX8HhLCIPFn6FvvcX4cXeywbGBTqmTPmf60PbKK5yU\n5LBtg4Wi3i6dlCSJjr592XXkSEQHniRJMQlisTT0j4SZyGVUqtqdkk+r1RqWzFq8YAH7CwpiKgNV\nVTVyWEAKZFycwaHaQ/45CgQCgUAgEAgEAoFAcL4hBLQ4iSSKmDptAIYokFrpFdAAVEgxFhacN8N9\n9bDymCZcmQlUkebncrmYmZfH7Lo6FiuBvmt/BX64fBlnHp6tiWheIYrqKvjdcrjdHZiTBM4fw32/\ngZVnAnNJB34G/DxJ5qqB2VgVhXaLhfzCQl6ZPz+shLVgQgFv//nPPGW3h4k886urjR9//XWWrlgB\nKQOR+vZF7ujgtpvHILW0JLSnWXdcX/kFBey02w0deH+VJFzZ2TGVNvoa+tdNmxaTk8sMI5HLjHhL\nPoPxjRtPGagkSVi6LBjG0IIICxAIBAKBQCAQCAQCwZcCIaDFgMvloqTkGRyOnXg8aVgsbgoK8ikv\nn6vrsRXRaSOhCWZ+JUGC0ybCQl848jN44LdWfpOeRarH4xeoVpeFl7YZOc2SMjJ4uK6OyUFhBBJw\nG6Aca+HHLyyh87VMrVH/6TZoccLd7vA0zww48iD8bJWFCztVMlSFVknmkuHXUu14i4EDB+pEO6MS\nVvshO5u/t5lv/79CaEpB7UyF5HZUVx9D8cdMWFrldZt9uH496enp3RZdgo9Xamcn7cnJ5BcUMLe8\nPCbB6v5HHyX/ty/xm1MdOoHzL8BdWVl0FRbqnHahgphvnfE09I+VaMck1pJPMyEu3jJQgIIJBdgP\n2bWQixBEWIBAIBAIBAKBQCAQCL4MSL6G8V82JEkaAezZs2cPI0aM6LX9uFwu8vJmUlc3G0WZhE8u\nkeWN5OYup6pqtV90sY2w0VDYYN687DcXw4kj/jFImQbfWw9DDbbfD7MumcWLy1+M6CoKdppNCnKa\njQN2YD6Va5OT+WemTFeyitwBUprCmXs8BltrZK/NprG2EUVRSEpKMtymeF4x9iN2fQlr0Hp4Ywac\nfoNIxxCgeMEC7P37G7ukamspcjrjFpaCxbkZo0ZhPXyYDzMy/OWX17W24rrsMtbU1kYV0YrnFbPi\nsxVkNKhctB8yu8CZBI1ZaXTeW2JYHitt3851W7fi7Oz0O9NaTpzAZbebllPmLFxI/Y4dEdfTHVwu\nl1byuW2b3vX3wAMsfW5pmHuwfKFeWLTl59PgEzaN5l1aSv3Onbr9+YXV4KRYb1hA1dtVot+ZQCAQ\nCAQCgUAgEAh6xPvvv8/IkSMBRqqq+n6ixxcOtCiUlDzjFc8mBz0qoSiTqatTKS1dxnPPLUKSpKhO\nm28OuQLn5xPxeFKxWNpJT1fYu/YKoFEr8fSpXwdkWDsIftRf21sEoeSZkhJmhzjNAC4iSpJnp5Uz\nx/4FSHQBdF0K6ueBJwU7405Da4vC4HHjIpYlRi5hBfp9AKcD9aHBx7CiYnFgnDhKBCNhlIg6UEmj\n6dgxGhcsQBkzxu/A+qy6msuWLWPJvHn86sUXI47r2OxALVRx5oJzStCx+ksmjDYIRGhvR12zhg9m\nztQCEyQJFAXmz4/LydXTfmk+TF1/Ju7BrRO36kSueMtAY02KFQgEAoFAIBAIBAKB4HxFCGhRcDh2\noiiLDf7iQlGqsNv/xurV/4vF4mbSpBsYun8o+9X9hk6b7Zs260r4bLYJ4PoQ/rZQ64+W4tHKOtsL\noeNJNmyYGXV+Ox0OFoeIZxLgJmLbKU5gBeTAg+0/gI9XkPmZStYBuKALTiTB8Suh9UQWrvuKcY0e\nHbEsMb4SVg1FmUxl5XIqvIpYd0oEjTAThL6oSKNtXomWnhk0ppKXx+HZs3l1xQp+9aK5689wnT7h\nMzXdeN6vvw4/+AEEC06yrIloMTb0T1S/tFB844cFYHhfJuVKhTq1jtKyUiqe0l6keJI/fcSSFNsT\nV113ORf7FAgEAoFAIBAIBALBlw85+iZfX1RVxeNJI1wVcqHlUI6lq+sjmprW0NCwiVWrbkJtvYT7\nBtxHjiOH7LXZ5DhyKBpYpHPw+BIatbEztGCBE/VwpFH7b0cFkIHHk0qkEltVVUnzeAw1q3xgo8nz\n1iLTQkjfqY5HuXR1Mq/VwsGT8J5L+++396XBvXMCziltAVqfrmnTKF261L8mf7N4w8miiYNhs5V0\n69QlRRovOiwp0gidIBS06em0TM15ZoBy/fU0qwq2/HwG3Xwztvx8ihcswOVyBWZrtk4JaG8znvfe\nvXDjjeGPDx8OtbWGcwl1cun6pUV4HbqLY7MD5TIFtqfBXwbCuiHaf7enoVymULm50r+tL/mzyOkk\nZ+FCsp94gpyFCylyOmMS8kKTYosXLIh4zBPNudin4NzyZW1VIBAIBAKBQCAQCM4fhAMtApIkYbEY\nebmeAWYD4WWd+/e76dv3JXBeGWiWf6p/jGMH70PFYnFHFIokScJtsRg6zeaiSXxngGkETFJrgfvJ\nxYm+RDKTpaxSPUzTrQj2ZmZq4pkBvnLK57wunkglrByQNWddGOHr7ElSpA+zctKuNBOXWHs7LF6M\nUvwQDUGlncEOL19wgek6L3VCTbW+B5qqQkqK8T5vvx0WLdKcaEH7NHJyJaqs1QhVVemgA9ZkwU/m\nwKjAXKiphj8soyOlQ+fWiif504zectWdb/sUnBsSVfIsEAgEAoFAIBAIBCAcaFEpKMhHlkO9XDuB\nSQZbu1DVlXzwQTENDZtobq6koWETdnseeXkzwxwuxmNryPIGCgvHAZHdE/kFBWyUw19GK3C/JLHi\n+uuZmJPDjOxsJubk8ID1Mo6wy7tFgCwcTEUvCKmAJ91EcAKQJBq/aOWyy2Zgs02gszWVofuHIh+U\nAw4tFaQDktbTrSNcAApep4/yxx4jd9065NragKNLVZFrazVh6dFHTY+Htql5Oali5hJ7/XX493+H\nvDy9w2v4cD5OSSF79Gi/W6lT7cuQfUPC13lRO33tK5BravT7cDqN95maCosXY3355YhOrnjKWruD\nJEm0nUQTz0br18+YPLhrNm0nzXvxdbcEsjdcddGOQW87+c5Xvm4OLJ9Qau/fn4ayMpoef5yGsjLs\n/fuTN326cBsKBAKBQCAQCASCuBECWhTKy+eSm7scWV6PppaogFFZJwScadMIdpVpzfIfprR0WZSx\nQUunXM+QIU/TIR/FNsLGoFGDsI2wUTyv2H/j57shnltezvLcXNbLcrCWw3pZZuU11/A/27ezqb6e\nNxsb2VRfz613/xRZ3hUyb5ULCC8FlQBLm4ngpE2CLqfVLxSalbDef/H9DMsejCzvCFtnbu6zlJXN\n0a0pESWCZmWWapcTqqvDn7R3r75HGWiutEWL4LvfxWW3+2/CVw0YgNT3Iu7L0q/zwSse5FDNbopa\nW3Xzvn7AAOTduw3nKn/8Mffcdhv1O3bQuGUL9Tt2UFGuT71MVFlrRFIyNOeZEWPyICWz+2Ob4Ni2\nTeuhZoBy441UbtsWk/ATT0lmLPtMFOdatPo6l6p+XYVSgUAgEAgEAoFA0HuIEs4oWK1WqqpWU1q6\njMrK5Xg8qRw9+gldXUaFkzuBxYbjhDbLNxvbYmln8uQb2PpeEyuP/R0K8ddfPl/3PK9+81WsF1jp\n6tOFpctCwYQCXnn7bV566imWV1aS6vHQbrGQX1jI6rIyXd810ES7rVtnUlenepNFtcdPcsawFLTA\n6cReXa1vuu+jajc4b8JXhqookzlwQGXixBrq99TrEyT/wxW2zsLCfObPf4VnSkrY6XCQ5vHgtljI\nLyhgbnl5j0oETcssB7vht8uA2Zow5EvElOVwh9frr8Ntt+mFNe9N+H7gFqczbJ2AccJlDE33I60x\n1rLW7hwrVVVJv/hSXBEcbukXX5LQhvuxuOoaT37BZTdeRrKSTMGEAsoXloeJp/GUZCYqoCIS50vZ\nYG+Uqn6ZAhd6UvL8ZVqnQCAQCAQCgUAgOHtI59ol0V0kSRoB7NmzZw8jRow4a/tVVZWHHlqM3Z7n\nFaD8fwFuBdaYPjc7ewaNjW+a3pz5btxmPTyLlf9aCUOC/tgBvA7kAVfhF9WkgxLD/jmMms01WK1W\nFEVBNijpDMbl8olZO/1i1hWZrcz/6D2mhCR6uoBvZmXx2Zw5qEF9uqiqhWd/B2c6oF+XNz20ADrK\nyMmZSX39JtP9BwtLM/PymF1XxyRF8fdp2yjLLM/NZXVVVbdFB10KZ1AiqrRPwrLFQucVyXA6A1LS\n4XQbuCX4/R/14sqcOfDMM6YpmTkLF1K/Y0dMN9wul4vSpUup3LYNT3Iyls5OCsePp+zRR6OuUVVV\n2traAoJIiAg31OGg8NpreW/jxjARMtbjZ8vPp8EntBittbSU+p07YxorGv4U2ij7pOhO+EGzlmR7\nSCb3n7m6MA6A4gULsPfvbyws1tZS5HRSUV5+VtapE60MhNKz2V8t3uNiRk8EwXMlRKmqyqCbb6bp\n8cdNt8l+4gkat2zRpdyeD8KnQCAQCAQCgUAg6D7vv/8+I0eOBBipqur7iR5fONDiRJIkUxcXHCc8\ncMBHbKEAAH9647/h7pA/7kITz64OfgKoV6vUKXWM/e53aDveH48nDYvFTUFBPuXlcw1v/qxWKxUV\ni6moCBezqKtjcpCYtUOWGTxgAFNOnGDDwoV4kpM5crARpa0TJjTCNWogoeCAHRxb6ei4Iqab52dK\nSpjt3V/QkpisKKh1dSwrLWVxRfda41utVqrerqK0rJRKRyWdUifJajKFEwqZ/8F8nqp4isrNlXTS\nRnLfZDKzsvko2OEVqfk/gCRxrP00OTk3c+ZMemzHPIqjTufYM7ihnzRmDN86ftz/Olg6O5k8diz7\njx/nplWr+FWwCGm3M3Pr1phFyEQEN0TCaD0ZycnIJvukukoLZQCQQLlSoU6to7SslIqnAudEvE6j\n3nDy+bbVlQ368JUNAqVLl8YkWhmNHevjPhIROtEdF9v5IETpSp5NhNLgkmcRLCEQCAQCgUAgEAhi\nIeE90CRJekySpFpJklolSfpckqQ3JEkaEsPzbpIkaY8kSaclSTogSdJPEj23ROErvSwqqiEnZyLZ\n2TPIyZnI9denI8sbDJ9j1CzfCFVVae86Ha7BfYbmPDNiCOz95DANDZtoaloTMbgglOBUxdVVVdQU\nFelCB2qKinh1yxYsp/tBUwrq/nSU41/AlMNwraoPEB2qQEEdbWc+RJIkXQ8ol8vFouJiJths3Dpo\nEBNsNta98gqTFIPETjQRbWdlZcTjFAv9T6lc+QXccFTiyi+0f1utViqeqqB+Tz2Hdx+mfk8929eu\nZ6jDgVRVFbjxPn06Yt8x9+ddfPrp5m4fc/D2qZpXrOt1N+uXsxg9dWpYA/RVF1/M9poaPly/3t8v\n7RunTzPvwAG/6AkBEfJhrwgZCz0NbjA+RKp/jUYN3T/67nexrAgJXVBVqNoFf1wOo9y6PnbKlQqV\nmyv9Y3cnXCHSOoc6HHS4W6L2HfStKbS/2Ctvvtmj/mrBx8uod1lzc3PYuRI8v+BxEhE6EW8fsfOp\ncX/B+PHmfQdDBGHRL00gEAgEAoFAIBDEQsJLOCVJegv4M/AemsPtV8BwIFdV1VMmz8kB9gIvAL8D\nJgDPAVNVVTWsBTxXJZxG6Hpd5c2kru7hIGeaiixvIDf3WaqqVsdU9mT5RhpdD5wKiFMq8N/AHRGe\nuDIbjjQSrLzJ8nqKimqoqFicgDXNRlEmaeNf0B+KnWZGOyz/mUJ2v3y/G27SpBs4+G4lc/fv95dq\nKsAUwDiDVGNGdjZvNjbqnCJm/dKMnDDxlIe6XC5mjBpFxuHDfJCRgSc9nWNOJ50PPaQlc4Yg79pF\nnyVv0eneoX88jmNuVmbK2nSYucB4vyHldxNsNjY1NJi9FEzMyWFTfX3Uufjm090y0+AxjJxme6dM\nMXR9Sdu3c93WrTg9HjzJyRxt+ISuy49rQsa/MiE1HdrbNDfa9W7S3ryYAZdc6R/7+IkTtNntpk6j\nyxcs4NOqqqjrnDx2LO+84+DA0AO6vnnSfokL3htE+kUD6erXz+8EfLe2lv0FBYFSTUWB+fPh6adN\nj01o2aD/eD1ZgmOzA0+Sh6TOJNo6+nLyp/eGlYFanq+gM/8I6rCA69OstDURpapRx/CWMPtIVNlo\nIoinnDbedQoEAoFAIBAIBILzky9dCaeqqlOD/y1J0t3Av4CRgNldyM+BQ6qqzvP+e78kSeOAhwHz\nZlrnCcEuLqNQgMLCfMrKootnvrFSpSxcB5o1RxdoN8udRKoO1XqQhfzRKLgg3jWVlDzjFc98/d5U\nSEkFyWnyRPDIaTQ0vI1mcFR5feWtvEodwR3jZKArypLcFotOPPMJYotjKFWMtzz0mZISv5OLtjZU\n4DvA8WXLqJs9WwtR8N2EV1WRu3w57e40QqWpWI65r09dyZMlmnh2VZALTwJOZcAY40TM4PI7VVVJ\n84SnpwYPlerxxFyOGEuZaSTMSuF48EEwcWap48bh3LjR30su599y+OzzfnD3HC0V1DfGu9vgpRdx\nFz2EO7gX39KlWqqqkdhYVcUlSUkxrbN4XjH7B+9HPZIK73uFO1cr6qkzfPHzWXwxOrDPlUuXwtSp\n+nAJWdZEtBjLBv3HyyegFnoF1HfT4JYSw+CKjl88AFuWgOT2Pm5e2trTktzuBC4komw0UfiSfEuX\nLqUyqOS5cPx4ys5ysERvcL7NRyAQCAQCgUAg+DpwNnqg9UfTOr6IsM0YYHPIYxuBZ3trUr2FUX+x\nePnh93/Eyj/9N9AIQ7w31oOAg+h7oPk4IEN7ocEfJDye1B7dbDkcO1GUxboxOd03iphnJVAdLHEh\nHzLVYNN8YAOaEy2UDbLMuMLAmuIVxHY6HCyOUB66vLISomyfCThaWihdsoTKzEw86elY2toodDop\nc7u5iRTqww5E4JhDQIhsbm7mjoIpfL73YzJUlVZJ4ou+MsoDIXNU0cSbGG/o3RZLzCJkPHTnOYY9\nwAAyM2NeT//+l/HZlOkwOk+3DYfqoXg2jB6tf/yhh2DuXCRVRTUQOVPT0mJa55qNa1A7L9QLdy+/\nDNdcE77P48eNBc7hw6G2Vr+9FyPRylBAPZoJo43FU8bkwWuZgFv3sHKlQqWjkoogear8scfYGkPy\nqxG+1yKePmLnoxAViyAc7zrPJedDfzmBQCAQCAQCgeDrTK8KaJJ21/EcsENV1X9E2PQS4POQxz4H\nMiRJ6quqakdvzbE36e5N19NPl7BtWy373rge+n0IKR445YL/U2GaOyCqqcABwJELHUbOj0BwQXdu\nXFVVxeNJI0yeaS/QAgOGGghUYWKeygUYu6TmAjPRnGjTCCxpgyzzbG4uq71uFlVVYxbEfMJVPM4s\nVVXp19ERIoNpMkU6UOF2U+F264QqFThJuOsPWmntfJ/BIwfjSfJg6bLwnTHf4e8vv8aK051MDVrn\nus4u7vsdHLkX6EtAlGxvi/mGPr+ggI12u05Y9BEqQhqRSEHD0IEU3EsuhvWc7FSMBaS9e+Huu8Mf\nT02Fp58m/Wc/I8tuDxM570xJibhG3/nS0tYB983RC3dG+4wULnH77bBokeZEC3LJmYlWjs0OzXnm\nH5uo4ikp6eHitQQeWe80jNWB5cNInElPSjJ191FdzeT8/KCp9Y4QlajzM9IYvR2gkQhE0IFAIBAI\nBAKBQHDu6W0H2gvANWhmo17h4YcfJjMzU/fYHXfcwR13RGoYdn5jtVqprV3jLQVtp7OzH30yXLhc\nxznxxuCAqHbaAu0p0LEICL95kqS/kZmZhM02IaZ0zvDnS1gsbsLu2DvKwbEVqItBzJM4gbFLygr8\nFRhntVKRlUWqx0O7xUJ+YSGvzJ/v73eW2tnJqc8/NxXE2oCWf/2LCTabvzfaydbWmJ1ZkiRx2N0W\ntn0+mg3SV3oa/Le1yLQQKk65wHodromHcV2F/7j8zf4yr53WRMLAUYHpwLPHoPj3kN4BF3TBiSQ4\nnOWks6ZacxyFEHpDP7e8nJlbt6KGpKeGipC6WcbRSy5WIjqQhg+H3bv1ZYkG61FVla5+/cLHiJaI\nmpZGRmYmhw4cAPQip5EDz2j9p8/00Zxn0fYZSRBMTYXFi7E+/DBZ69ZFFK1UVcWT5AkTwqKJp5xq\nCz+pVbB0ha8z1pJc09Lbd9+F55/TdjAm4O6jugp+uxwKbtONkyghKhanVSKF35649c4WvZHwKvh6\nI8qABQKBQCAQfNn585//zJ///GfdY06nSaupBNFrApokSSuAqcC3VFU9EmXzo8DFIY9dDLRGc589\n++yz5zxEoDcwKgV1uVw6US058xSTb7+BbdtWsn9/ui64QJL+RnLyY3z0UYXucbt9I1u3zowpix5R\nSwAAIABJREFU0ACgoCAfu31jUA80ACu0VcEbd2K9+F0yBqRhUSy0NJ7B1baLUDGvhQLWYWc64S6p\nnbLM9++5h8Ve95hvnaH9ziZgXDXqQnOxPdnezlRvM30VuAd4C71o5cPImdWSDOskmB6UqRHskAt2\njq2XZYot/Wjt/LY3ydH7l5Q7oeBTfZmtBFknMCxhdaElZvz+c/34/3PSzY+WL6Pr4TmoUZxMvvTU\nZaWlLK+s1ImQq8vK/K+xr+9avL3kYiWiA+n22+Hxx6GrK6Izy3SMWFxsbW1h54bR62y0fgX4xpAh\ntMS6z0ilmnv3Yrv0G7S6mlBPd4KSjOoJT5+UJAlLlyX8pL7UCSbiKdVV2t9B9zz5E5nCWyI7DSPd\nKJuJM4wfD6oCf3waXkvV3G+n2+ASJ/zAzYa3tcRh33WbCCEqktNq0+TJjB87lo27diW0hDFet965\noKf95YRYIgBRBiwQCAQCgeCrhZFxKihEoFdIeAon+MWzGcB4VVUPxbD9UmCKqqrXBz32J6B/aChB\n0N/PmxTOc0HwDVFAWNvpDy7IzJT56KNfoijhHcbiToqMkiyanp6uNWIvXoTdnhcitgG4GMg3WSl9\nxjRVDXdJhYg2i4qLyQspS1wE5AGhIy8CRhMuULmAiUCJJEXdp6qqZI/MhsNHWHlcE9F82/8FmJ0C\n6YrMsAGX+sWp++bP56mnXtId8xZ1N667Q9JJFRi5BN47E35szdYE8DrwTF4ex2U5rkTM4POiubmZ\nKXfcxsefHUZNS0Nyu7lYkvhNw2fMNLju18syNUVFul5y8TDrkUdYeeGFxiV/77zD8L//nbaurojr\nMU1yfPllyM017j1WVUXhkiW86RXR4j23AGwDB9Lw6qt6sezll+Haa8Odc+3t8MgjSHfeqRc4a2ux\nrPhNzEmZxfOKsR+1B1I/VbSwkDez4K7ZOteXXFtLn988R+fFbdCREUgn7dfKMPkyarfUdvsG+Iq8\nPD5bssTc9VZ0J/ygWS/2dUD279IYljpA52K8/9FHecpu73aSq+nr7z3m3HWXJlxGSNXsKeeT2OT7\nfB508800Pf646XamCa9CLBF4iSeZViDoKefT+6hAIBAIvl70dgpnwgU0SZJeAO4ACtGK+nw4VVU9\n7d1mCZCtqupPvP/OAT5CK/n8PXAzWu+0qaqqhoYL+PbztRbQzPB9abHZJtDQsAmzIsacnInU18cW\ncGok0GnJonN0X7gjiW1DhjzN924ayu4NG3QuqTlBLikfE2w2NnndZP6x0dxgv0QLHfAJJfnATpNV\ntqKVh14cUh5qtE/bCBsNkxrI/DtctB8yu8CZBMeHgvM7kLMxh0N7Dhl+IfTf5I4aRNP0prC/D34S\nDnaFz3ECWsSsWZnpxJwcNtXXd+uLaHNzM4NH30jHA0VaTzF/+V011y5bRlVLS1jRb/A+u8OsX85i\npeOvcO9sw5K/WQW38eJzL8ZeThh0kyft2IFl1So8P/+5TrSSqqsZsnYt37vuOt7r5rkFUJyWhr2k\nREtb9dHervU0+/73w5xzQ9as4ab8fDbs2uUXijKS4KNLd6Hmhr+nygdligYW6ZIyXS4Xo24exT7l\nsJa86hPFkp30b72QzIsv40xKCpbOTiaPGcPWqioOFBToBCSppoZh69ZR89Zb3br5VVWVzJEjcS1f\nbrqN9OD9qDMP6MSzS38Lq47pnZMbZZnlQaJld85bW34+DT7nWTBGgQ5e5NpaipzObpUwno83eUbC\nV8uJE7jsdlORM6e0lPqdO3VjjJ46lbpp0xJ6vpzvnI+vZyycjXmbitP07BqKhy/r6yOIDSHaCwQC\ngeB8oLcFtN4o4ZyFdj/1Tsjj9wB/8P7/pWi5kgCoqtogSdI0tNTNYuAw8DMz8Uxgjq8pvmHz/8BW\ncaVzxposarVaqapa7RXbluvEtvnzX2Xpc0s5eAF0yirJCuT2DRcaVFU1DACwAquBZcBcCfqlw0kZ\nLmwHyWM87wzAlpHBm4cO+Y+NGQUTCrA32XFOUXBOQV8id1ArkTN7vu9xw5I8oOViWNes9TzzrxOI\n/Arpgw7iZcodt2ni2ZiQNMu8POpmz6Z0yRIq3Po0x+7u07f9xnc3wg9aYMsSLS3SpOQv0thm5XST\nx47l45QULlyyhA8yMvxhAde3ttJ62WUs+PWvsb4YWZwzO7cAyt1uti5bxsezZ2sOOkmCfv2QCgu5\n4JVXsDocfjGrcPx4yjZsCOvHZRthQ/2O8Q8SwUmZwXOU+l4E034SJnJc6hU5/O7OBQs4OGOG3g0n\nSahjxrBflrvdA0uSJDwtLRHLY5NOtnEm6E+ZWzTxLLSnX2gibrznraIo5n30zEIkiK2EMZizdZPX\nnWvXtB/d0qWmgQ5G/eUeeeIJ6qZO1Ts2vedLnaoy78knefHXv+7Oss47vqw37Wd73j0tA+4uX9bX\nRxAfIuhEIBAIBF8XEi6gqaoqx7DNPQaPvQv0XrHq1wjT5v9+Aumc3Rk7Ema92/Im5lF3VZ2WOui1\nrNgP2dk6cauutE2SJNwW89CBRcAfM6HuIe8Gz4F60tzF5UpKimmd5QvL2TpxK3VqnVZS5yu/+0Qm\n92AuZS8Y33gEUzChAPuhoJI8L87/D+77Daw8o4lovtkcI9IrFGiA7+tfFg8ff3bYOM0SUPLyqMzM\nDBPQzJruG+FyuSh5sgTHZgeeJA99zvThePtxSAG+5QbcYYsLTYo0I7j5vW/ti4qLmfHJJ1rpZZs+\n8GH9gQN+0SYS0c6tXS0tXLtiBX3eektffvjeexEdVX7ROjQUIJhOOHbyFLb8fP9NZEZyMvsLCmIS\nxXrr5ldVVS7t6ODT6mq9+86LXFXFBS4nxxRABlS46GPjnn6gT8SNhdCb66MNDeFiXrQQCUnCk5wc\n07nV2zd5PRULTPvRPfSQVjYMUfsiAvxpnQPsLxrvZMwYXnvgF+dMQDN6nbr7Q8GX9ab9bM87YsgL\nxHUNxcOX9fURxI8IOhEIBALB14X47soFXxoKCvKR5Y2Gf5PlDRQWjuv1Ofi+iJc8WaKJZ1cpAYFB\n0lw5dVfVUVpWqntefkEBG00Eo7VAyxD8AtcX34B1Jvtfi4xywSUxzdVqtVL1dhVFA4vIceSQvTab\nHEcORQOLwnpXmVG+sJzcf+YiH5Q18QhtjvK/ZNKvu5qn/204ucl9GGWRyU3uQ+uALDaYrHO1JHHU\n7WJY3z6MSbEwrG8fxo+8nubmZm3YCKXXiqKgpqVFvllKTyd0BKOm+0b4BFH7ETsNhQ00TW/i0xmf\n4j7lRjdo8O5NkiLNxl9UXMwEm43vXX45E2w21r3yCpOC+pYFjzJZUaj8r5exjbAxaNQgbCNsFM8r\nxuUKb94f6dzaKcv87LbbqN+xg8YtW6jfsYOKoGTSSHPXhQKE0gG8kYX73odoKCuj6fHHaSgr48Nj\nx7QyVQOUG2+kcts2IL6b33iRJInL+/Yld9ky5F27vKEYaOLMrl3kLl/OJYpEzlrtmrii8gqypbSY\nnJPR5uO7ubb37+8/Ll3f+hbU1IStzx/oYISqYunoiOnc0t3k+bb33eRNm0bp0qVRx4hnPQ1lZdj7\n9ydv+nTD8zEUx7ZtxudEair8+tekv/wyOQsXkv3EE+QsXEiR0xkmQqiqSrssRzxf2mWpW+dLd3G5\nXBQvWIAtP59BN9+MLT+fWY88wqxfzorpujWjN1/P3qS35m32muoCWoyf6L+GEnlenO11Cs4dpu9d\n6D/PBAKBQCD4stNrKZyCc0t5+Vy2bp1JXZ1q2Py/rGx1xOcn8pdox2aH5jwzILi0zcfc8nJmbt2K\nWlfH5KCkyPWyzIIL+5PZkU762i4sioXjLZ3cRyYr2c90AtuuReZ+crGcSI15nlarlYqnKsLK7OJ5\nftXbVZSWlVLpqMQje7AoFgonFFL2gnkiJiHrXC1JzAXsx1p0PabWvf8ho3MGwbCBKMkKyUoyBRMK\nKF9YrruBlmUZye2OWJbnaWsL/JOgpvsmLqdgdIKoDwkYDBxEn0Lqm1MMSZFgnpLp63tnhATInS4a\nClx+l5SRuxHMz6141m+GmQOR2jT4yZzwZM3MzJgdIaYJpxB28xvveTt+xgyuX7GCbUuWUJmZ6S+P\nLXQ6+XZ7Ox89+KAuJXeCzYba5jbtO1jf2sotgwfrwgXmlpeHidCGjoXbb9f6zqmqru8cF10UVwmj\nGb1ZxtZTB0ZUoTQtjYzLLuPQli3eoSO8zu62iOcL7rbwx3sJMxfSyupq+O3LWtl3CobXbTT3bayv\nZyI+zxL6mZjA8zBW12PB+PHYd+827IEm7dhBZnKyziGbiDLLhK8zyPVs6bIYfv4Jzj7nyuEoEAgE\nAsG5QDjQvqL4+pEVFdWQkzOR7OwZ5ORMpKiohqqq1YZfOF0uF8XFi7DZJjBo0K3YbBMoLl4UlyMg\nlKilbVKgtC947qurqqgpKmJiTg4zsrOZmJNDbVERuw418On/fUpjbSOH3jtEZsoNHKGGuyjianIY\nSTZXk8NdFHGEKrq6MrrtzOkOPhGufk89jbWN1O+pp+KpijCBK9I6F190IXZVZRo6wx7jgZs8Cin7\nD3PpgWbkQw38sfJ5Rt08Kuw1uvbyy6Cm2niS1VX06Zus22dNUVFYYqUZjs2OcJEIYCxQBexH78A7\n6C2DLY0uTj1TUsLsIHELtDeprqAhQ1GBE30JvJsZuBt954DZMa8pKuKVt9/mmZISJths3DpoEBNs\nNhYVx+6IMXMg8llmeDltnK6qgvHjkXfvNtxU2r7df/Prc/cUL1gQ87znlpfzwrBh3NLezqHmZhoP\nHOBQczO3tLfz4rBhzPHeAPvmYubicwGTgKUuF5saGljT1MSmhgby7HZm5uWFzcfQsZCaCv/xH7Bv\nH33uvdfvtJr1zW+S+9ZbyLW1epdcba1Wwvjoo1HX2VtOPt/2PXVgxOMSiuaGTJXORLz+U6UzCXcb\nheIb28yFRF6eFjiyO837mHbdfjzwY0YMsZm6b4PHj/h6njrFsZaWbl8XoHfDduc9wYhEnofxuB7L\nH3uM3HXrwq4haft2klet4qMpUyKO0Z3rIqHrDHE9NxQ2YD9qJ29i+HuL4OwSz3uXQCAQCARfdoQD\n7StMrM3/IThBczaKshif78lu38jWrTNNRbdo6ErbTJp9+Ur7gudotVq1nlYVxm4wf+N+ixtIx0kF\nTs1rELSj6L3eevMX0VgDGkLXOaxvn7AeU74U0oeBP3SC1Ol1pbXC/Rn7mLdwHi8+F+h5tP7P/6Ol\ncKpqWCJm3xfs7K7ZzcCBA03Xb9anCDAXRPsCt0Paa2kM2D/A1IEXiZ0OB4uVcHEuH9iA5kQLZa0E\nLUPDH1cuU3j59f+hcsd7Ya6K0GNu5HxTgY12OzO3bo1JXDRyIPbp6sNx64W4jc6F4cNh9259DzQv\noa6qR4uKeOnGsXQ8oEBeIHCAd96Flf/JR7Mf1qWWxttjqCld5a4hKlmfBxJoWy5WuSQ9/IbIzMVX\nBJSi748moZXYKkHhAhDl5jo1Fe65h4sbG/ls82a/4OxyucLCJQrHj6csxjXG4+SLRqjrp8/p0xzv\n7OyxAyOSSygep90PCwtZ+dtloBon4l6XcSkTbLaoLsF4MUsQVex24yeMydMCR/D2Y2yFSx3wnCfc\nfZt/1WB2HjzEwIEDgSivZ3s7PP447h//GHeQkzGe6yIR7wlGJNJRGo/r0SygJdNi4aOiIuMxVJVx\nU26ltUnC40nDYnFTUJBPefncqGtP5PVm5npWrlSoU7UfSoITjsN3JZxPvU2i3rsEAsH5h3gPFQj0\nSF/WXhKSJI0A9uzZs4cRI0ac6+l86SkuXoTdnuct99Qjy+spKqqhomJx98aeV4z9qEFpG5o7afg/\nb6D1c2vcX9C7O2+Xy0VJyTM4HDu7tc/eRFEUxqRYqPXoj9UiIA8IXyU4JHjgIiuf/atV93hzczNT\nf/gDPv6sESU1Fbm9nWsvH8Rbf/qL/yY0+EPR5XLxTEkJOx0O/431jZMmcSIFNr670V8203KiBddP\nXKaCqPWVTLKkG+jsTCU5uT3mY6uqKrcOGsSapqawv/kExGLwO/O0Ul24fwAcuRdNwPPRAbyZBXfN\n0ZUC+pquh95ALyouJs9u10IKQlgvy9QUFbHouefi+gLhT+fMz6fBV7oWjPcmn5kzo86xuHgRK1Zc\nj5q6BzK3QXoytHVCuwXmTdZEtRDk2lqKnM6ojZuL5xVjP2IP3JyGJNAWDSwKuzltbm7mhwVTOLr3\nH2SoCq2STJIqsdck5VQFJlxxBVsaGvyPmR4X7eCRU1pK/c6dhnOOR/jVrXXBAuz9+xvf5MV4vHQl\niUGiJQ8+CM8/3631RBvb7LyNNM6om0exTzkMpzMCibjJrVxe18l/dp7RiZ8bZZnlubkRRaFYfoQZ\nPXUqddOmBVJlFQXmz4ennzaf7Nz7YdoBkCHzJXitWZ/w6sMBLBtxPe/s+T//Y6av58svwzXXaPMI\nIdbXOZb3hGjBJWZEOg+l7du5butWnJ2dUcspo15DCxdSv2OH4Ryivj95x+DOudD8HoE2EBvJzV0e\n049qibjeAGwjbDQUNph+5uQ4cqjfU697WKR/JpaYfoRNwHuXQCDQONeilXgPFXyZef/99xk5ciTA\nSFVV30/0+EJAEwBgs02goWETZt9Qc3ImUl+/qVtj61I4QxIuLRv60dnyB1T1e5h9QY/0IRJwzj1s\n2Ost9Eu+3mk3yXSf55JhfftQ19mleyUmAOavDgxLTmLfaY/pcQruJWTUS2bStydx8O13mbt/P5OC\nbqzXAfdnQvPP8fcpYg1wDTDEYEf7gTdmwOk3/A/Fc2wn2Gxsamgw7a81zmrl4qwsUj0e2i0W9re1\n0HivS5tbMNvT4JYSGG3QM8vgxs1svy7gaeBvSUlceckl3XLrxHSz7PHoXVWPPqo798OvT6/KNTAf\nXu3eDbS2icrgkYPjujkNu569241cAu+dMT8O49PTeKfV5T9HE3VzHXw+d8qdpr0B/XPv4U1eRNEm\nN1cTQyOsJ5Yb0dKlS6ncts3wnIgVl8uluSE3V9IpdZKsJnMFmcz/v4+YEqMoFMsXaN96Zj3yCCsv\nvDC8T92cOfDMM8YC8uuvw7a/w4B+0N5GRqOTRpebDIP1qEBuch/2dXh08+uWmBnluoDI70UqMDEn\nh0319QZ/jY7ZvKUdO0hetQpPUVHU81NVVQbdfDNNjz9uup/sJ56gccsW0/MtljG4/wk4sIXgN4hY\nf1RLxPWmqiqDRg2iaXr4Dys+stdm01jbqPsxSIg5PSfeG+hEvXcJBF9XzhfRSryHCr7s9LaAJko4\nBVo5lSeNSI3KPJ7Ubv8aYtZcP8PyDT5qmY+qfl+3L0WZzD/+4eZb3/p3nM6uiC4xX6+30tJlVFYu\nx+NJxWJpp7Awn7KycMGmpOQZr3g2OWyfdXUqpaXLuu20C6W7x+vi4dey7v0Pme4bB4j86kD/KDp4\nsHjmFz8KA2Lm6/+9klf36x1uEjAd+M9WuOvv4PR18p8C/NE7sWAR7QDgGETm6UvIYjAX4OEEFlqU\nAv7xj/v9xzbScckvKGCjietjpyzz/Xvu0TW0L55XjL3JwN14JBNGhQsZEN68WlVV0gycUz7X22zg\nP7q6kJqaulXCVf7YY2ydPp067751X0Q2b2a794tIcDlpqMB5zCUBbYBvf94XLj3+xs2hX9COnuiC\nHWkwyq138Xl34+tRaJiqizYNZDihRqzU5vPO07p5RDwu69ZRtnZt1GOrc1p1ZEBqOnS08fzWl9n0\nziZqt9SGv18YlLHFUwpq2hj99ts1R2FIAIK8ezdD1qyhY+zYmJq0W61WKsrLe9z83igUZYLNZnht\ngVZqu7yyErwCmlnzf/vu3WyaPJnxY8eycdcu/3qajh6B3/4ufODhw6G2Vu8Ga2/XwiL+/d/h7rv9\nY7dWVzN22TKqWloIfSUkIENVdD8GGL2efTo6OJ6SYlw2DTGV05q9JwTPxZc22+3PxHjLKdGXZCai\nRDKWMWjrIPSqVpTJVFYuJ5oBLxHXWzxtIHzEUtr6XFmZKEmKQKTrP1IZtOpxQXsT6ulOUJK1fwsE\ngqh095rrDXoaiiQQfNURDjQBEIsD7Rbq6zcnZF/mrhofPuniIQjqhBOLkynaDU1vOu3AuAwyXsdS\nc3Mz+VcN5jenOpjunWk0B9qIDCv/62w1+KuesHI9L4Ofg4Mnzce/uj988sugB52QaU8i6wxcgMoJ\nJI6rFtLUy1nFQaYS7GKTuZchJKe7GHKRxfS4qKpKW1sbM/PyeNgsJdMrWgWLTWHuRgWoHALPrjQ9\nDqHODCO3SaSy2XhLuGL9Zd7Mrck/gcproa0KgqWFaA60kLJBU8dOTTX8YRnc2qIX0VTIqcyh/v2A\n08Y2wkbDpAat+fuRTE20am8j+aiTP37h5gcG63dI8PMBaTQedREq5vXEsTDrl7NY6fgr3DdHE0yD\n17NqGbMKbtP1BgwlWspjKFEdO+3tpM2Zw4CsLP96Jo8Zw7aaGvYXFJj+kpuent7rN/ORSqR9zMjO\n5s1Gzclj6rRrb4dHHoE77wwIhZFKNX1i2fe/H9g+Uonlrl0ULVlChdutnz+Qm5zEvg5zm2OsZYmx\nlNOGvifou2vCLTk5bO6mA63b8w5xziXCxRlpDHbVwhInuMPHyM6eQWPjm90qa4+XaG0gQsvMox3H\nlLvvZmJXV0L7/8XCuS7JCibR5e6mlQaHZHL/mRuWiC0IcD6dF4JzR6Jc+YmgJ+0BBILzgd52oIkU\nTgEABQX5yPJGw7/J8gYKC8clbF++5sjmrrdn0Hw/+hxKzSX2MKWly0yTu6IFBsTqtOsOvqbTeXZ7\nTCmEZgwcOJCdBw+xbMT15Cb3YZRF5hNJK6c0Yi0w5Yc/imlswwRNFS7oiuxwywyOweyAS1+F1zq7\nOKh08Z6icFDpYoZ6mlUcYBpK0KsG41GwsY8X25rCjsuMUaN4bNYsf8rd9667jhHjxrH9vvsMUzJL\nnizBNsLGoFGDsI2wUfJkCW+vfpuigUXkOHLIXptNztoc0k97hRQjVJWkU6d054pRsuROtFRJIyYr\nCjsrK80Ocxg+V1H9jh00btlC/Y4dVBjctOncXcEHcQhQUAd9S/UDO8dDda3hPo0aN5smIo7Jg7tm\nQ22afoxPZApvKfT/W1VVOuiANVlaieyKV+HplbDiVTrnlvCjb2TxOoFTRUUTz+6/CJIuyQq7PmM9\nLmb8qbJSE89G54Wv597ZvFa5Juw5LpeL4nnF2EbYuHz05dhG2CieF1uyYtS0uX79GHDhhbr1WJKT\nNfEs5Jgrw4fzcUoK2aNHdzslMh4kScJtsURMs3VbAk4e01TR11+HH/9YK9X0rUeWNRHN6LikpsLi\nxfD8b8hZuJDsJ54gaft2w/AMACUvj8rMzLDH1wKXDB8edY0QObU21obm+QUFvCFJFKelYRs4kEFD\nhmAbOJDitDT+JkmMKyyM+Px4Pkf8n4lxplaaJWvGk05rNga7qmD5OnAbjRFbQI/ROruDWcKxUcJz\nLMcxKyWFN7v5+RwrvvW7XC6KFyzoURpsogh+7/N9hpq998WbKmz22RWaiC3QiOe1EHw96GmSd6Lo\nrcRygeCrhCjhFABQXj6XrVtnUlenGvYSKytbndD9SZLkTdA0qsvYCSw2eJYLRanCbv8bq1f/b9zN\n/yPvE2K5KYjEMyUlzPY6p/z7RBNb1JAUwmgMHDjQ3yxbURTcbjffGz0a9u1jmqoG3F2SRMWwYbzx\n619HHVNVVeMETQlOJEUuvzuZFPhj5hZYdUzf6FsCmiEsPRQ0OfRx9AmaEpCvKJzat49v7dvHEgJm\nq42rVrE8N5e/ffih35ljVnpqP2Rn68ytVL1dRcVTgVK168fn82FNtSakhFJdxQV9k3QPhSZLQvSy\nWV8Jl6qqcbmYIpWNOTY7tPUZMUSBtNeh4zl8B0BqH0nyijl4ZAICTYQySNPyQ9AnInp7FOYezKXs\nhcD2kiTRdhK4f46+v5wkQV4eZ5jNj3+3hJIWtz/N8/hQaM2RePCKW7t1XMxQVZV2tY9pqS5j8mhf\ntTIsKMP0PJq4NSaXRKxpczohKvSYB5UwukaNwpWgUo1oToZIJdIbZNkvCkX8Ar13r1Z2GYpRqaaP\nDz/AmiJRv2MHiqJw+YQJNEX4cu5KT0dB+4XPFxZS3K8vOx1vma4tmESUB9//6KMMWb2a9qIi1OAk\nz+pqfr9iBQfmzw97jlH5tVk/vvBlx1+SmYgSSbMxMujLR188hBpWTGv+o1pv9e8xawNhlPAc03Fs\na/O/t3fn89mM0PUntbfT5nRy8p57znlJVjzvffHcQPvf5yJ8dilXKlQ6KqmgZ8f3q0IiPocEXx38\nCfdxXnO9RSITlAWCryrCgSYAAr3EiopqyMmZSHb2DHJyJlJUVNNrjfWNXW9mHb98ZZ1j6er6iKam\nNTQ0bMJuzyMvb2bMv9r1ptNup8PBpAj9heJxLOnnJWO1Wnmjpob3HnxQ58x678EHeaOmJv5eMiG0\nDIF1Jp+FayVoGRr4d9aBcKEsUp+2nRiXQfqENV+RLgQEx4e9NzSGfbci/Lrt2/7kycPwX8ugapfe\nVVG1C/6wnBMnG3VzsVqtrK6qoqaoiIk5Odyanc0nSUmmbp1W4B//+pzcFAtjUiwM69uH8SOvp7m5\n2eQZxrhcLhYVF/sdeMkfN5K5Hi1FNBQJ0rLaueKKW/zX54MPfsSh3bsoam31u3tyFi6kyOkMuzmL\n5aYoiUwGOgaS48ihaGCR8Rf5lIyIolVnUiafPATvz4ZPHgLX1TLXfHqNziViRLd+zUxLj7geUvWO\nukS4JOJx/Zge89dfh9tuC6RVeuerjBpF3bRplC5dGsvqgfgcLnPLy1mem8t6Wda5BNd7S6TneIU+\nU6edqkJKivExv/12+Otfoaoq/Jr77XJ+VDgD0N7PIrr4VJW2zk6u8bpvc5P7sGzE9ezxX19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X5sEspnounJlhT4gZFAtksIn29lk07nM+cDrJ7zEau1HSjWzheFm+l7vmeAmi07ubFnJpe60/Al\nJDBs3DieECQi27XB+CbjfLvnYogh2ogRaDF8IxCJ+b+T8kuxx9AIFP2SEXaCC9plDGxoSz3Fxfko\n9vVzMVPDqSc5tbW13DV6Ikd27iBNhnoJ4rt0Q5LeRZZvN/yuWsknSZJtss0pwi3Era7bgucXsIDw\ni3bb/mKahmkXC2aT3O/c+R0efeMt5NPNmhJWs3CBW2VZSNp5boZJVVB8FM1x3pUkftWlCxlLlzJx\nyRLLiRiYLKxO1AsvXfpHsOiosY2jAgHO7NrFVZlXEkgb6IicDTsGUlIdL5aDih2NYiE/QHoZZFRC\n1xY4EQd1ueAZFuC9N95g599Kwqr+NGqw57Vj7qVZs9hQWho6RkNSkuXiPO7UKS1p4aBcYdavZ1HZ\npxIuDwANmvOv3FupIQQt70Xfz6BwLExtgTx7Chf9QuRwdXW7yKmzMQGOxoKjtraW3kMH0/TIZBja\n1lctmzbB64XwfWMpseugiwfuuMOW71JHLYplWabTBRn4LO6hpAsy2kVyOIFp+eUNN9AMDFi50rI8\n3A7xxakGw1AXqficjgsRgXrVpT15/52/20pOtsKE/AksPLAQ+aO5yqaAKvlZuq6RR3rdzfzn5kfU\n52aqF5anagM9JCms75o+5CVnYA6+oCLN8H1j4I4T0tKMbDX1Y7sU2IuxjPNQuimBbJcQPt/KJt1u\nt+35zLmC6L0omotEpNZ2oFg7myXp4SB8zzdB99eVudUYvEj1XsPGZCQhOt90nG/3XAwxRBMxAi2G\nbwycmP+bmW6blV8aPYbC50Q6CS5wArHfUSHwNCI13K5dPm644Xt4PC0hwunb376aj99exMLmRsbQ\nZui/oq6Gh6S7OSS93UqimSv5xo0bdlbLZkOleg6umwiyLJPi91v6i6W3EFbhAOaT3NrZv+bucWP4\n6Rc7SZMD1Esu4pD4U7Nf+JtC0i4JDv0QHvxdAv+vaybJfj/1cXGcaGjguZMnGXX8uFAhZphYihZW\ny4AvgVxtOzIqzShhhehL9MbzlXcZTvvcDJIkkRwI4LVaLNc3qP4bpD0SXTZ3YWmXpSxZs4T4M/Ec\nbThK998HJ6htp/n3LfDENnilRauoM1P9idRgXq+X2/PymLp7N79QlRJPSE2l1DThs5yuSW3qFafl\nCnbJtuD3ze9FN9LxAgaUvYKnLLzpsHAh8sYbsHmzUmKmw9naDQ/3HI3GgmP0XXco5Nm1glJieSq8\n+yyMbTBVjtl5znfEoliSJDISk/BZ3EMZiUntUmY5gdViWb7+eva98xcyOp1GPt0MgURkv7E83Ir4\nYtMm6KzaALBQ8TkZF2YE6uebN9F76GD2bd5Kjx49Ir5uIQXi5bsJXN+m6FLafiVzFs2JuM9NPcNG\nNsDrhUhMaysF79/f0f3sNHDHTpJzqN1maYNm5fvXAX8GSZaQr2gNIggAidabMHYI4fOhbNIq9ft8\ngXpTSfRe1M9FIiHsnSrW7Hx//pzI7y+7MHvPW21Myrt3Uzh7trJR3IpoJFx+E3A+3HMxxNBRiBFo\nMXwjEe5FKSahzMsvjR5DEuFyIiNVYIWD2O9oA6D/DMCLLBfzj39MQU0tvPvGlbxNI7fSFhcpAWMJ\n8Kp8hqkZ0zjjLrZU8rW3bNYpZFm2vG4iolCkkpIkCV9CgqW/2Mk44z+G2xFTX+sePXqwdtv/AW1e\nShOzspBqasR/iwlplwgnEuNCyafBpDh1kqvVRMx0YTUaZSGCdiHStcmS4yEdf6iRVqXKTnD3reMo\nNiWiNtH/kl40lCbid/mJ88fR4Gng5PUnOX7Z8dCEK70QFjUYJ6g7gVdbtKSgleoPjGqwl2bNYmqr\nsbj6GG83NNC/sJD907QeWGwqhz/N40RSStv3HZQrhCbhzcrE26Coux6OHjuqWbTmD88nN/cFKitF\n92Ixn77/nkGBKoJwIRL0aZLlqPo0hYMThXA0Fhw79x9UiBMR8vKQirvQq/TCqCnHovlumPDtb5sr\nrbZsYeJ3vqP5rKNKhOwslr1JAbzjqpUkU0FAC1gTX33Kyhh+/T2sLF0Z9lo4GRemBOq1eTSdPs2A\n79yEO6ObaXlY0AbBDB1ZlmdKuCcBd9aRuui3odS+uMZGGrZs4WQrwaDp29JSOl91FSNycjRq3dw9\nuVTKlZpFrrRHIvHjRHaM1AbuOElyDut1p/Njcx1wkds9l5suvklz/euaXJabMHYIYbvXJ9qboudL\n6aEVRCr2S6U0Zgrei/q5SCSEvVPFmun3GxsJ7NxJ0aefsqS8vMP71uw9b7UxOSoQYF5JCfpylfYm\nXH4T8E0sVY4hBruQgn4v3zRIkjQQ2LZt2zYGDhx4rpsTw3mGnJwRVFevxmw1m509kqqq1cp/yTJZ\nWROpqVmm+97TKPFUIgXW+0yevDkiYiGcZ5SxLTIwEUVWpIe4jb3pzF5OmxJI/eI7scd/ypaSTymb\n3aAj26ZF5eWnX0Qraas7MF43L3A7oCUKXa5V9Os3z6CSerqggGuLihgtKOMsBe7rAyf/k7aJe+uO\nmHqR5xQjcnJY3ep9pocMXN4F9j2m+4cKFykf9MJ79CskSQp7jJHZ2ayuqgp9ljMwh+rx1eJhfhrc\nb7vJ6JYRmrh02VvH9nqveRvJZh9Vmk/V90ok8Hq9imH+mDEGw/y+ZWVsbk3LlGWZKTOmUHSoSEsI\nAr2fhb0C8m8EYH6XwxVd4Ct9nwOZyzM5sOVA2D6vB7IuSKG+u7YkiyE+Mle3HQOgYHoBRYdNyhX2\nupjcY3KoLLPXt3rhP7TfoKhbIcGkeDg0Ebiy7R9c+1z0qejD8IETWLnyM8f3Yqhca9gwqoPKMzUa\nG2HxYuLXr+fiXr3aSIiZM6M+yTUGtOQT7n42Ox+7CAQCJAzoT2Dh70y/45r8Y/yf70SSpA5dzESy\nWNIoBwVKK8uSJxvfdwLTMaScHEy+B+6s1XwcHP/qEkav16sQX+vWaYkv1ZiLlrm4LMsk9M6m5Q9/\nFI/9n/8cbr/dQCBf/t57JMbB7ppaxyWf0VoUy7JM1pAsasaqNmd0C/jg8wzalNz6vh113XVUlJYy\nvbKSfLWiyOXipT59uGLkcFZ+2kZapXdKZ0fvHYZnMYC0W2LA4evwnMHy2mXdfDM1P/+56bmlTJvK\nhfJx/C5/W9r0bCOZVfDkkxR16WJKIE/2eByHYhh82iJMVbRCR96H0YJGxa4iSi97Cb40r+zVzEWc\nXB874yLzV7/iwEcfhTachN9vbFQ2fu64A85i3xre8zIMmgefWeQwTcjMZOmBA/+SJJkT/KsShTGc\nn9i+fTuDBg0CGCTL8vZoHz+mQIvhXw7hDfC15ZfmHkNPoJA2ARRJT+QKLDtqi5DHiKEtVmo4kTIt\nQFfLs4e0QPgddXBWNusUxnJNUIhC0W+8BEwlXKBDsI0PzZzJsNf+wMtNpxhL22JhOS4KEjtz24jv\ns6Z0TVR3xIaNG8cqnXosiOVA3UVoypKodEFpPzIuuDA0UQxXeprs96Pe9LAsG+wEaRensW/LPuXv\nJYmnCwos2uiiDr0Cr/2lym63m81lZWKVSCt5FmyfWTpb1wSQmgwfhymyDl+qG67P04DL/T62f8/X\ndlDdMYJwUq6QHd+F6Uf3GxR1Y2Uo9sO9+8FzVds/BC4LUCFXcEtaI1VVq21dD/0zJz6+gWMXNImJ\nj+RkeOABLj5wgP0ffhj2ueAU+gVq/dEGvEeGQ2AY6uecXdWj07HocrmQfD5LlYTka4z6eQfR3gW6\nUwVeR5QIhcqJLcsvy6G7R/tZEwT2ByhaXcSSNUs05x5OgeH0Oqu/r+7zJqmJls5u8bVfvBjuvBP0\n5WH9+1P55ptwzz0aYk1f8mmnLe1BSPVyGtiaoviBJadCY4PS14N9YusBXd8+XVDAhMpKsaKoooLN\nt9yiSc/MGZgj3AwAkPvKeL6sMU3bDLXbSpnk8+HyNiB1y0RKSoKmJuS4FMMxoGM8ozQ+bWFSFSOd\nF3R0Gm40IFSxA11c1u/W4FxEkiRH18epYs30+4sXK+TZWe5b0Xv+hMtSfI4vIWaWD9FVZccQw7lG\njECL4V8OkRjgiz2G3MAS4FHc7p+TlpYZNrhABCtfr9WrJzB8+FBNsmZaWhwu10oCAXXt2TBgFVoC\nyYxCcHHC8uyVQAGni8Wz4/XmhChUEAgM4403ZlBSsl7Th183v8m9fMKFlJCOHw8JHGM89f4bSY3b\nYTn5jwRPPPMMt69Zg9xa9tCmKpJ4SO6Jp/pWeHkldPLD6QRoHI/UfCMTH9zRdvYWpaf1QFV9Pbf0\n7h0qv3E11IsTBEFI8jzxzDN898MPObNnD7fKsopYlHiIPnjQl0lEp1TZTrmCqY+YBCfijaPCilYG\ne6W60Sz3dVKuEHfspGnJx1jEIRfq0lM75JnomYN/UNiFS0eQZ0KfvsrlUJoHDeUoz1oFdgJaIkG/\nzB58YVZKXF5Ov56ZER3Xlh9nFBbokZT8dPF6uaymhuTmZhoTE+nitZBJmLRdT/zl35hPn08/pQLt\nYplN5fDneTBR5WnVBCwG8qDl2y3USDXCc4/2u0XY53+QxGP/iy/g/vuNB1m8GO67D4YObfssWPIp\ny4y5+07+b+36qLbbDPk35lP89/+BH01TzPSDfb55EywqZNQ4o0q+rcnK+VoF66jLzDRl5hYMitrT\n0QymZGtjI0yfjvfBB/EOHRo6HzNT+I70jLKbnhwJOjINN1oQblpJiqWAXVLI6fVxWmIu/L7ZfUvH\n9q3oPe9PrKdMauBWQUVXpInqMcQQw/mNGIEWw78knBrgm/t9radfv1rKyz8mNTU1oom+ua/XMPbs\nOcWePTegTtaUpPdITJyC3y+3kmgSMA3IB86gOEEF23EM0TSnjqtZwTbGIlYaXdJ/gOPziDbEXm9O\niEJQSju/h9f7LF6vuhjueuA2PNyOR1lytv29LFNS8ioLFkSXFHS73SwpL6dw9mzmlZSQ7PfTmJDA\n4FGjSF9bwZHK8QROtJWQuVwrye3zIk1NQ8nJGYHfn4Kr/gxlkmSYiHlRrv5zXi+jvSqzfEli0itQ\n+xOMCYImnm41qTL35spkHFEUWp44OHaRjKdGVhgp9TE6ICzCDFY+YnW5sGKrotBSYxjwPmL/keXA\n8YtBZQMoVIOFUw6GjmGhKAvCjom8LMu4W1qcK+dsLlrB/JmDZxRs2gJ5Qw1/01GBAaY+fX0CwG54\ndzY0qZc6HRPQcktuP+oLCzk4dSqBvDZPO1d5OT3nzWPkhAm2j+XE16gjFuh2CFQ7BuDhjiEi/hbt\nW0RuUy6Tjh1jpWqxXFe7D68+yXQjisOAOlUxSuSEFYR93tmjEE5qDzRZhk6dxISyxQKda/PYuajY\ncbsiHtMJbvjvaTDU6N+GPA1OnAj7u3bVzSFVvk1PRyuYKZNYsKBN2ac6Hyv1UEd5RjlJT3aCjk7D\njQasiFKzdy6ISSEn1+eZn/2MD8eMYU9Li8HWoU9ZGXPKygzf14wjML9vocP7Vv+eb2ho4Pa8PFy6\nzdOVLhe/6dePJWY+gDEA9qphzkdEs0w/ps775iFGoMXwLwmnBvhut5vy8iWtfl/zLM31nUJMFIFS\nlvhzlPLQICRk+Taam2UGDHgZj+c3obaMGnUTsJGVKxeEPktPT2XHDr1aDTwsZRKXU0yTsYQxqTMb\nyt6L+HyiAfMy22DZrIxColkThUofPo62DwEuxMA+qP5/RyWout1uxeR/gZZA+VnIS26e6npew7p1\nLhYtGk4gECRQ65nEAF7la8bSRgdOBmZjYpbvgfveFXu66UmeWb+eRWWfSgKXw0nQdmlFpYrMiG5Y\nhNfr5aVZs9hQWhpSzw0bN44nnmkrYZNl2TQRznMzPFQBxfWSRjk3QJJ4LDER/H5G6yauhZdfTv9O\niRx9djdpsky9JHFJ//78pbRMcz+bKQdXulws6NOH/xw53JahuV1Eqnqzu2gFi2eO72dQOBamBiDv\n7AQGWC1QyQ1AcomOQOuYgJbPP/6YHXV1PDV3LiXp6fhTU0loaGC8x8OvfT5uX35NFRkAACAASURB\nVLPG8u/V6XSGJFOBesZp4mo08dKsWTxuYgAeEISRiGBF/FXKlYyUmqhav77Np2p6AUU1unt3P3CT\n+Pgdde5gMuZu9cHLhSCrQkEAPB6jMs2KWAOF7ElOtrXwi4a/1qqNG8HUjP9aVj71lOXf23nm6MvM\nrNI5wwXuBGGmTKo7fhyvICUU7KmHouGLF/y3aCjtzNpot1TxXC2grYhSz80wqQqKj6KZi0SLFJKb\njiEvmQtvp4X8ReXO9chST8N3RePo8KFDtLQjWCKaKclmm6fDxo9nyZx/XbN8q03CcH1bW1vLxDvu\nYMfBg5CSAj4fV/fsydK/h/eXPJeIVihIR/kuxnD2ECPQYviXRCSEWLT9voJeVeZ+bOZlibJ8Gx7P\nq6Z+R5rFXN7t7N6Njij8B6mXXcOLSaf46Z4vSAtAvQsuuWoAG8reO6cvKHOvN2grm32J+PjpXHxx\nb0uiUNyH4Yv7IlmgOx0TBk8a3dgqKHiaioondCqhNGrZwX3cQ477E7LSUmhMSOBEXR1jTMqvxgI5\nh92cKM0IS/IYFpbq08kNEJ/2GhcnVkeNPAZrNcyE1asZOnw4W1etIsXvp16S6HrMRd1YGa6S22bt\n1S6OxXVi7YN3seCjjzQT1LUzZvDa88/zG9XE9ZpRo4hft46ZO3ZpDbP/bwf3jxypUeBYTX7fm+Ms\nnc1usmRYv7w+xmPbXbRae0C6oW45KS8Po1vZCkelUJE8E+0sUOnUlvwKHaN6DCpw0oAFPh8LfD7D\n00GtwAlCNMlNc/dg960ThL5Gu5qauGH8eDzNzaHJ9VHPKWjGoBANnn8kC/Rw3/902TLTcr3RgQCF\ny5YZUuH0sEv8hXyq9N5AAIl0CDlhBdMxlwbcVQdvzoVX0nGlpeFqbKTF50PWK9MkCU6ftiQ/7Hjm\nRaN8N1pKJqtnjlpRFPK1cuDpaAW9Mgkg6+ab8XagesjuIjcaSjurdlqVKkqffkp6YiI5w4ad03RO\nU6I0CQ5/R6LwywG8fNwTlhSyu0kmSVJoI4/LA0CDpv8r91YKlan6cTRl1izHScMdSVqYbZ6ea5yt\nVNmZkyfz3PznbPVtbW0tuYMH0zh5skaBuG3TJnIHD6Zyq7W/5LmC3c0zW8fpIN/FGM4eYgRaDP+y\naA8h1q6Jm24BXV9/EuMMLbwFupVKSuM9YUoUrgg9hM+1RFrUL2KvNwA3Lte1/OQnEvPnP21BFAYA\nkVQHxKWgCpws0O0SIk4QvHbmykQ3J1nKiYyRbNv3AQATs7KQTAg0CchKTWPbZ/uQZdn0OtshMy7u\nlcH+ze9Fday8NGsWUwVqmGGBAKf27OGGPXtUBcywAheTlnTl0Iep0Lkl5Bfnb74Rf8oOVlcZvev0\nE9enCwp4oqJCbJgtUODYmfxG6jtWVLSKNWtuDyVLyrJsqnp73+WiICmB+uzm1sU7jhet4T0gU+nW\n+SKq1n9oy7+rPQsOOwtUTieE+iqaqkd9O/QKHP3TWK/AMfVue6dZSX3To7ERedky/qFLc2TTJvhT\nIXy3tbxR3YjWBbod2FmggnKftxyvs+Qsz9Qdc67M0XWenvwSeQMdPnmYFrmlXWWAThDanDEbc2nA\nf/rILunGV58piauZgzI59CedMk2WISNDuXYiz7xN5fTvlaX5TREMKr7WNjkpYXVqum4GK6Xti7m5\n5ArInA+WfMDzC54P6+loF8E2RuN8zOB0kWsgkFTjxmzTwi5BN3PyZF4bOpimnzyiHVvr1sKrxeyY\nOlVT2up0IR4NWBKlX/ejdO2nuN1uyzlkuE2y3LFjWbVxY6iv6g5VEfi+eCPPjjLVaXBBsI3tIS2c\nrCHONXnWkamyQ8eMYfettyqK2NY+X7h5M8VDB+Mfdgh5vBy2byfecYdCnuVpNy0CeXmckmW+e+ed\nbF5/dvwlnSBaoSAd6bsYw9mDpE50+yZBkqSBwLZt27YxcODAc92cGGLQLaDzaVtxPQDcAZrMPYAR\nwGrMVhfZ2bdQVfWhozacTztfQZj1i+L1NhO/f77K661tER0kHNTHUYjCDSGisK7uBF7vVox96EUp\nBS2gzTPOeGyr/jJrt8u1in795hna5wSyLJOVNZGammWm38nMnMCBA0uRJIkROTmsrq4WjpR64Hq3\nm4syMiwX1gA5A3OoHl9tuqDNLsmmanuV43OxGnNmbX8auBZj8S1AKS7uZTIe5qNmG7KzR1JVtTps\nm6z6SwZGZmcLibj2oKDgaYqK8oS+i5K0hAFDXsDT/E+NGfsFTRJbV67U7O7f/cgj3HHfveza9zmB\nJHA1wZW9B/D+u/aVo1ZtcbneZ/LkzZrUWjU06tbggkO9sNrnot+X/WzvkhZML6DosLgUTKqUSF3d\nk7TE/1AR/9NsqXKcXrenCwrIM1HgvO9ysXnyZA2pWjC9gKJDRdpJrgwsz4WXBP5Xb7wBV12lTYUL\nYu3H8LeXQUpuS1C8yAPHfLgb3KRdmGa5yFEvUDWKSpeLef36GTzN+ibFs7tZ7LMnA/0S49jTdEbY\nT+oUxur8atPkx+xV1s8KWZaZMmOK6bV37XUxucfkdi8URMSi/4I0Ps3dgdzXOL/V/67mPA+nh8rJ\n6HoS9naGAh2xtqmcxIW/5Z7v3cmazz6zJFCCx05fAxmV0LVFMWevywXPdyD7g2yqtoV/3hY8+SRF\nXbqI1TZbtjDZ47G1cPN6vRTOns2GkhI6NzdzKjGRa0aNYtkXX1AxbpyQiNCXJEcD0TqfaBzb6/Uy\n5OYh7AkchFNpbeO8cz19XT3Z8tEWwzwkRNCZ9FfQL7dgegEL9y9EPpasHVuBRrhvulb1GKXzjwRe\nr1chvj/UEqUzpszguYULwxKFZs9WL3B1RgZfP/GEEsah3lT4cyFMrBMqczOXZ3JgywHAmozyer1K\nWee6dVo19cyZhmeo4XmuJkpNnkV2Ny3OJ0TrvS3Cwz/9KcUXXGASxLMRPpoLN2iNdEV927lXL07/\n8Y+mBHqn++/n1NdfR9TGjkTOsGFUB4lDPWSZ7KeeosqC+NO8W63m4aX23gsxWGP79u0MGjQIYJAs\ny9ujffwYgRZDDFGC+aLVC4xEkmYhy21kDtwP3ImRWNMucr/pCEssDFiEx9OiU89ZL6LVZZBOjz1j\nxiSee644rKrMLgkRKXJyRlBdbY9AtZqgjgSeQiGiwi2srcgMJwtau8o8WZaZmJXFspoawzGs6WO4\ngmy+QjuJCJKKYD6xtvpNUPpseEoKF3Tr5mhSHG4BabyewRm6F1LzYPxOxUxdMKENLriMpK0Cp6Rt\n23EeN3hA5ua+yPBRuaxat0pD5iGh+Sytcxpf9P5CSyAF2+NwrAgn9K2qOvX566Hu8/aqQYMk1ONm\nRs+t90rYSe6fesDrbxkn0dOmwUsvGT9vbISf/xxEyrTXC+HOOiVF12KR44T8k2WZrEvcvHrUJzQA\nL5Xgx91SOHDYq+lb/ULxVHoKG72HYZI4+fHhcXfwyvxXwvZ5uGvfXkWEiFhc6XLxk6QEvh7frJBo\nFr9reCaqlWufQ8aObDxnAgSSk3E1NtK3R3ea5Tj2TpgQlnDKHJQJBw+x6KjiXxlS2Uow6UKgZ3dq\nttXYU7eGIW3s3gN65VRaYiJfjB7dIWRWR5+PCE4XuRpVjYrkkTZvpu+KFWwu03pmWhF0Unk5qYt+\nS1qGi4SWBOpO1OH9gdf4SvhbD1goeIaYtPFsIqg0c3KNzDasClJSKJo1Swlt0cOEcOE0uBd3J6N7\njqPS1rDv53AbAjoy2+mmxfkC4cZPK9q7aZF2ZV+8Ra+YjlsevQfuqNV9riWEAoEAKf37c/p3vzMe\noxWdfvxjfDt3dmjVTCS2CVk330zNz39u+p3MX/2KAx99ZLSB0D1zjx7+Ct/3j4htHWgjkM83McQ3\nDTECzQQxAi2G8w3WhEg9bvf1ZGRcrDOR30pFxTRh0EF7FE7nE8ITRSNNvd7CwYooECnN7KrKZFmm\nd+9bbLU7Ujgh6MwW//djRsGKVTXRWNA6VeaJJtcyMBEw19/BIDLZzn4gOIkK3kMXhSVQzCb0QV3i\nFLQL2jJJ4jd9+/Le5s0GtYHdsjlFUfgW6cwig1K64ucECdS50vDcvkPxdNNBP6GNJmkrUmyOGnUN\n67YvoyK3ou36nwbeAm5EQ/DxB+BBorJLaqZwmDNb7KWjn3DmX3cd65Zvo7JyervUoGoFjlr1N2mG\nUWlhOsl9LQUenWVMc5w9G0REg5UyTbCIFC1y7Coqg+j1rV74D+2n+JiSohe8pMsleOhCSOh+KV//\n4+tQn4gWihNTUyl58kkTtUE5D584wSsvvCBokRZOrr1dqEu1zYjFMpeLF/7f1XyNx/J37T4Tg8SC\nE4XTpd3SeOWYV/h8LpXgkQw3+4/W2zpnJ2obUV+ZESI8+ij89rdnhczRE+KRnE+44ztd5Nq9niFi\nPQxBFyIRAsDbwL367wArcuFF8xRX0UK8IyF6z9X36MG2CRNs9YvZhlVOjx5Uv2VOFBoIlyZgcQb8\nSKtYay+xGiSzD51uhh8INgT+VEj3TokaMtupYvl8QUepm2RZJuHqq2hZaE588cRDMLbS8Nt6Quhc\nKdDaGwAQlpyfPZuqDRs0vyd85uptHTTHiawSxCnOx2qlaKOjCbSYB1oMMUQB1sbdAGmkpeWwb59W\nPdO2yI1u8uf5gvD90r5ETLthEcFjz5r1UivxoyYoJAKBUeza5eOGG76Hx9NCc3MyR46cale7w0a5\nO0iKNTO6twoXGBUIMK+kROPrJfIpcuprY9WHu3fLzJ5dqCF5RObVEuYxD17gRcDHYQZxqUJCkY+H\n7Xi9z+H1tmnt9P5iVr8JSmbrY4jTTAO7d/Ps9OnMfUVR1Vj5uty+Zo1mB1qSJOLiPHQnj0XsZowq\n+XZFACathUOXY5gs6f1ezH3xFO+/kpJ54bzfQxAGV0wvUMgz9e50OTAchTxTIxmr4U+T1BQyBQ93\n77rdbhY8v4AFWBssm/kXvbZ5M/JRDwSGqRplPuas2qH3ujP7TeEkVwZSfMrndtIcAb74Au6/X9yg\na/Pg7XSUu0GBekwE+zfF77f0NNMHIEzIn8DCAwu5t0rmwgpIbwFPHBzrA/XZEo/2mhj6vplH4edp\naYpiTtju8MmPQdi99uEgWvycqapimkVYwm+Oe6gKU6pt95kYVEOUrlunjBMB9AmSGc3aZ40aY2V4\n2m99znqPObWJerjSNhEhsltEiKSnixeEAFKUDP1N/Jjsno9dROIXZ3k9r7qKNx5/nJK1a/EnJRF/\n+jTHmpst+4tOqcpzwoVCoulfchKK8qmDPOCcwuw91725WVn0C6Ae5yJ/SVqP4U9Nte6rhFSlj1yt\nf/BBKvxwmva5E4HHlBrBsdVwEnhoGgzVhYVcmwfyVBoWLdT0+YbSUtMgFvXc6nxCR6bKAuCzHrec\najD+tsrrMvi7V/fsybZNm4TKRFd5OQOyspy3LQyiEQBgFQoiCq4w80xTNqWmiktebYZFRYJoJYjG\noCBGoMUQQxQQ3rhbnPwY7eTP8w2R9osTOOlDc4LCiywX849/qLVJIxy3206ZmYbMCkP+6RdQ6sU/\nWIcLNAB1R48yIifHoJ5qz4LWKcljZl6dCZShVc8FFWKPAb+kBYma1mCBYiZxKYe4ATsEitlvrsYs\n91ZJM3367b/yzO9+Z0ksmAURZHdpZPr+XdyKrPn+WKD4GNy7Bjx6wzfVhBasEnuVL0dKNoeCK0TJ\nivuBm4ztohnz4X8aju2vo/eg3o5Niq3abjbhlK+9Fqa6YO5z4NMuoNRjLhKjZ0eTXAk4A9xep3z+\nts7XSJ/mKMvQqZO9BXfwK81w9NhRcgbmhPq2i7feModBH4AQMga/YjeeUarxu0ei6+auLK1YypI1\nS5Rjf1VnWCjaWfxGQqxEutlgRXJeV1hIeV0d+lEnIhbNYJfkk2WZpsREy35pSkgI3c89U1KR6s3D\nXzKTUw0ktJ1FTri+sk2ISFLYtNF2G/rbMG6P5rzHySLXMuG0sRF+8Qu8Dz6IV+3f9eij9kmES4G9\nGDcnunuMzwqTNnYUrAh0gHgH97/ZJllCQxii0NNM5vLsEGldd6oTXhPSXk9OW0F0D52WOinKMxGu\nzYM//1nVNNnxpkVHIJLna3tTZa2OnSydwWsybtlUroxr/d/tkUh3Z2oCSr59zTXsXLiQ07KskGhB\npWF5OZ2Linhv61bH7QuHaAQAOA2usCLnuTYP/pgOss+gerabcOwE0UoQjaENMQIthhiihHHjhlFU\ntMqk/Cp88uO/GnkWRHv7xQms+tBaDfcSMBVtaqezJE+rFMbVqycwcXguW1etMpJZOvLPDgkXPE/R\nzi+0EVG/9vkY4/OZqqecjrlIFIVm6rlrRo3iN+vW4WpNy5RQlGcihdhYoJiD3MtsPLoptIi0E/2m\nLz6elv0HkGTxrrIEuHwn6Tm4J4mBRCGxEIR6BzpY2hV38ghjVOSZGmNluLBCQKDJEHcmLtRX7SWb\nI0pWTDT5Of3iL9isJuAt8N/STPUV1aaL4khgOeHMGwzpT6nFWq1o4Kj3Kw3h5CRxzOkklzQU0vEG\nH+DT9suiQiR5mkL4hVOmQduCO4gmYDH4rvfhu6LtN7v8FVbUK/eBHitdLq4fP771cOZK0zh/HA2e\nBk5ef5Ljlx1Xjh2AQS8aL7+txW87VTJ2kuKC52NFcu6eOpXZc+eywKcdGCJi0Q6svi9JEg1HDln2\nS8ORw6FjnEpKsiQ+D/oaNCR0/o35fLJtp2LoH+EixzEh0r8/bN0qLDFuL5nT3rS5SAgKJ4tcS8Xa\n4sXwve8ppYRtfwCDBsHmzWJ1pp5EuA5YjKKyyiV0P0sXNpJYtBC/5FLGtEkbo03Q2FVxOr3/zTas\n+ns8VFsk2XaiiapttSESOevmm/G2k7QXEgWBAMyYYUkIpl58SejYZqq60OkT2bPFDtqboGlIlVWh\nveqmu8ePp/h1QWLxpnJYMA9uVL0PZYU8S9zQnR2Pjtfci29u3crll16Ke/lydhYXQ3IyNDYyICuL\n97ZutR2W5AROlMNmcLvdlC9frpSeP/WUtvRc92y2JOcBJImU1Iu5sCSBM3Fn2p1wHA7RShA9X3Eu\nBCgxAi2GGKIEJyV5/044X/rFWg23AaM26QkUGiqA2qJf3+7QIs+0tHEY9Xv2cf2ej5kbOoqYzLIi\n4aJRqjgqECCgUk9FtMMZodJSXzoHbZ5UQZLrq8OH+WVLi/C3xxLgQkoMBJqZMkuk2OubkILccsp0\nUnzCDbXjak2JhSAagNqDB+mTFE+6LOMBMnBZ7lint2DstkoXXZMuCf1nJGSzmnBtbk4mMbFR6A0n\n3J22UppdB7wD6Vsh4wh0DcAJF9TFKwmC5GpPsL0R7HYmnKQm6hqrBDT48qvwqVQedsm8SCa5o24a\nxbrydVS4KrSeWQdc5GZ046YTJ1ipmlzXHK3Hv2kr5Ak80DaVk3bYwxXzWtMZk8FzPYa+PXkbPPQ7\nKK6XuFWWQ8+QlS4XhX36cLnUJCQQ1aqqKTOmGM2lXXAiCeRTxss/zuOhyKzMpp3EipUyafXNq5k4\ncDhbV60iubmZxsREPktKIvCKOLAgkJdHSXq6gUBTE4tRxel6U/UQm8rhdBuBYvZ8BlgOVHf3cnK8\nN3T+xcvfhtuf1JJZDhY5siwLS88sCZHvf18JuWhp0YRcSJs20aesjDllZeH7xARCxWsr9OXrQbQ3\n+dDJIhcsFGtmpdfB/pJlXShIOfx5HkxUjcMk4E5wv+0moyJDm3A5bwbPFxUZ2jjjnXc6pMzKqYrT\nyf1vtkm2s+GfSrk7AsLlz/NI7aIlxJyW34ogJApcLoVEszh2UnOzpszQ6t7tqGeLXcWmFULqY1ns\n6dgeddOLv36RdRvXsWfJs/B2Wpv6ulM9V2R25+aed7CydGVonKenZirkmeB5theY7PHw2TPPhDYh\nOwp23vN2FdV2S+klSSLu1CnLMZeRmER1eXWHnz9Eh0A833CuS1JjBFoMMUQJdv24vqnoaJ+yswEx\nQSEDIlWVG1gCFBIXN5NLLslRJXn+0aASq6s7KSxtTGcWr3FAU6oYJLP0pYBO/cXMdn4/xLxUcXQg\nwKw33uTNkp0RpRlGU2mpJrkCgQDfvfRSJJP0TAlIx4+R7QmvzAopQpIzWOGtZSyCBa0EdX2DJ2JO\nLATVfS+cOaMJIhhGwHLH+mRA90GlC0r7ceKC5NDHTslmr9fLkCET2FPths5fQSc/eBL47aIUVq+e\nwJYtyzTXVLg7bVZmBHQ/DovqdQmC2Pd0cwI7/kU0NKHp4aRZMHon6Xshowy6trQSUbkBduXsCpF5\nZs8uO7/ZrVNnqjZUa45RW1vL6Nu+y65VnxNIAlcTXNl7AO8vfS+0ex78/sMPz6S4cLEicM1TGQmX\nl3PpvHnsOO4jLdi3J2HSx3DoMl3fJkHtj+Env0+lT0pGaIE6eNQoarat5eO6RWFL5MzIjLpcWLEV\nQ2LnMz4fJYWF7J+mUtRZlKvoYbUoMFUm9Qxwctkedu46wFfp6fhTU4n3emkKs/jxpqZqrJRCyapm\nysIIIcsyqV3Aq/fAMyEFzJ7PK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rElN5fhx45pkqmjsfHRXoWUwQbFquQ7QgWWKEQG4IxZ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9i6AFp1O9J1wwgYMLDtpti/BDHxxrMn2czhcrkkRAEARBEARBEITTAnWaBIvvaxD1/mpnX+IX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pB5eSDAiAEYOoqIiIiIiAxWPRKgGWMetywrA/g+MBz4K3CVMeZw801GAKOOu4sLeAAYC0SB\n94BvGWN+1hP1iXTVJ584mzZffx2eegquuy7eFfWQPXvgllvglVfgjjvg/vvhLDqmwuEwS+9fyvri\n9UTcETwxD9eErmHFvSvw+/3nXGZDtOG0RykPhg9S3Vjd7n5ej5eRaSPJ9mczMm0k07Ond9hKmeXP\nItWT2u5+MWN4vaaGzceO8f+qqnix/CBHIgdwA5f4/dyUmUlhIEBBIMCQU2wkFRERERERkf6t22eg\n9RbNQJOe9vbbzqbNhgZYvx4uuSTeFfWAWAx+8hNnOcCoUfCrX8Fll53VQ4XDYWbNn0XpuFLs82yw\nAAOuMheT353M1j9vPWmI1hRr4uOaj097lLKyobLd/ZITkhnpH0lWy/B9X3aHYCzbn40/qWvhXZNt\n82o43NpdtqWqiupYjCTLYmZaGoXNHWYz09LwDdgzvCIiIiIiIv1Pv5uBJjIQFBfD9dfD6NGwaZPz\n64Czbx98+cuwZQv8y7/AihWQmnr6+53E0vuXOuHZOLvtSgvs82xKTSk333Ezn/uHz3V6lPJw3eF2\nj+VxedoFYJMyJnUajAWSAud0TLQ2FmNbdTUvNgdmW6urabBt/G43BYEA3x49mssDAaanpZHk0gZM\nERERERGRwUoBmsgJfv7z/8/evcfnXP5xHH9dm81mmw1b5jSH5XyeKAkV6UjnRAeKijmfcj7llKIQ\niko6KqXkMERUzmdJP6qdmPOcZuy8+/v74yslp+Gee5v38/HYo+2+r+v7/Xz1u/3ydl3XBzp2hKZN\n4auvoGBBV1fkZA4HTJ1qNwgIDoaffoJGja75svOXzcfRwnHB9xyhDhZ8uoBFJRf9s1rMrzgNSjU4\nZwvl398X8S6SLeennUhPZ/XJk2dXmG1KTCTDsiiSLx8NAwIYXbYsjQICqOnjQz4FZiIiIiIiInKG\nAjSRMxwOeyfj2LF2gDZpUh7stBkbCy++CCtWQHi4/bC+vtd8WcuySHNPs7dtXoiB4ELBxA2MI5/7\n9ftFPZyWxsqEhLOB2a+nTmEBxT09aRQQwPNFi9IoIIDKBQrglic7Q4iIiIiIiIgz5LV4QOSqJCfD\n88/DnDnw1lvQvXse67RpWTB9OvTuDYUL23tUmzRx2uXX7V3HkRNHwOLCIZoFXg6vbA/P9qSknA3L\nViYksCspCYBQLy8aBQTQrUQJGgUEUNbLy2UdQkVERERERCT3UYAmN7xDh6BFC9ixA777zu66mafE\nxUG7drB0Kbz8Mrz5ptP2pcafjqffsn7M2DaDoPJBHI06eu4ZaGe4RbnR4p4WTrnn3yzL4q/k5LOB\n2S8nTrA7NRWAqgUKcFdAAENLl6ZhQAAl8ud36r1FRERERETkxqIATW5ov/9ud9pMS4NffgG7YUce\nYVnw0UfQowf4+cHixXDvvU65dKYjkw+2fED/H/tjYTH1gam06t6KO+67g538pwtnlBuVIyszcurI\na7qnw7LYcfr0OYHZofR03IAwPz8eDwqiob8/d/j7E+jp6ZTnFBEREREREQEFaHIDW7rU7rRZpgws\nWAClSrm6Iifat89ebRYRAW3bwttvQ0CAUy69af8mOi7syKb9m3ih1guMbTqWIJ8gANb+sJZBIwcx\nb/480t3S8XB40KJpC0ZOHYmfn98V3Sfd4WDLqVNnA7NVCQmcyMjA0xjqFSxIu2LFaBQQQP2CBSmY\n5w6rExERERERkZxEf+qUG9L779uNApo1szttXmG2k3NZFnz2GXTtCl5eMH8+PPSQUy59LPkYA38c\nyLTN06hetDqrXlhFg5AG54zx8/Nj4tiJTGQiDocDtyvoZJmcmcmGxMSzgdmahASSHA4KuLlxu78/\nPUuWpFFAAPX8/PB2d3fKM4mIiIiIiIhkhQI0uaE4HNCvn30MWKdOMGFCHuq0efAgdOgA338Pzz4L\nEyfaDQOukcNyMHPbTPou60taZhoT7ptAeN1w8rmd/wuXmJjIwDFjmP/zz6Tnz49HairNGzdmVP/+\n561AO5mRwZqEhLMH/m84eZI0yyIgXz4a+vszvEwZGgUEUNvXF48rCOJEREREREREnC2vRAcil5WU\nBM89ZzcKmDDBXqSVJxoxWpa9jK5TJzsN/PZbePRRp1x628FtdIroxJq4NTxT/RnevOdNivkVu+DY\nxMRE6j/0EDsffBDHyJH2L65lMWXjRpY/9BALvv2WbQ7H2fPLtp46hQMo6uFBo4AAxoeG0igggGo+\nPrjliX8xIiIiIiIiklcoQJMbwsGDdqfN33+HuXPt7/OE+Hh7L+qcOfDUUzBlCgQGXvNlE1ISGLxi\nMFM2TqFSYCVWtFnBnWXuvOScgWPG2OFZvXr/vGgMjnr1+N2yKNurF7RtSxkvLxr6+9OheHEaBQRQ\n3tsbo8BMREREREREcjAFaJLn7dhhd9pMT4eVKyEszNUVOcmcOXZ45nDYK9CeeuqaL2lZFp//9jm9\nf+jNqbRTjG06lm63dsPD3eOyc+f//LO98uxC6tUjcO5cNt92GyFeXtdcp4iIiIiIiMj1pIOFJE/7\n4Qdo0MBuQLl+fR4Jz44ehdat7Raid9xhL6tzQnj2++Hfuevju3juu+doVLoRuzrvovftvbMUnlmW\nRYqn58X3xBpDfm9vSuXPf811ioiIiIiIiFxvCtAkz5o2DR54wM6YVq2CUqVcXZETzJsHVavC4sXw\n+ef2KrSiRa/pkompifT+oTe1ptXiwKkDLHl2CbOfnE3JgiWzND8pM5MRu3dz6ORJ+zy2C7EsPFJT\ntVVTREREREREciUFaJLnOBzQu7fdkLJjR7sp5X8aQOY+x4/D88/Dww9D3br2qrPWra+pC4JlWcz+\nfTaVp1Rm6sapDL9zONs7bKdZaLMszXdYFp8fOkTFDRsYtXs3tW+7DbeNGy841m3jRlrceedV1yoi\nIiIiIiLiSjoDTfKUpCR49lm7UcDEiXanzVxv0SJo3x5On4aZM+0g7RpXcv1x5A86L+rMsuhlPFLp\nEd6+923KBJTJ8vy1CQn0iIxkfWIijwUG8kZoKDeFhdldOAFH3bpnu3C6bdxI5YULGblgwTXVLCIi\nIiIiIuIqCtAkzzhwwO6uuXOnveqseXNXV3SNEhKgVy/48EO491744AMombVtlRdzOu00o1aOYtya\ncZTyL8WCVgt4sMKDWZ6/JyWFftHRzDp8mNq+vvxUqxaNAwLOvr92wQIGvf468wYPJt3TE4+0NFo0\nbszIBQvwy/XLAEVERERERORGpQBN8oTffrM7bWZm2p02a9d2dUXXaOlSaNcOTpyA99+3v7/G7Zrf\n//E93RZ349CpQwxoOIC+Dfri7eGdpfmnMjIYGxfHuLg4AvLlY0bFijwfHIz7f2ry8/Nj4qhRTDxz\nT515JiIiIiIiInmBAjTJ9RYvtptQhobC/PnXvEjLtRIToU8fuwPC3XfDjBlQuvQ1XTLqWBRdF3cl\n4q8I7r/5fpY/v5zQwqFZmuuwLD45eJABMTEcS0+nd6lS9A0JwS/f5X/rUHgmIiIiIiIieYUCNMnV\n3n0XunSB++6DL78EX19XV3QNVqyAF1+E+HiYOhVeeQXcrr7PR3J6MmNXj+X1Va9T1Lco37X8jocr\nPpzlYGvliRP0iIxk86lTtAwKYmxoKKW9vK66HhEREREREZHcSgGa5EqZmfDqq/DWW3ajgLfeAnd3\nV1d1lU6fhv794Z13oHFj+PFHKFfumi4Z8VcEXRZ1IS4hjt6392Zgw4H4ePpkaW5McjKvRkfzTXw8\ndf38WFW7Ng38/a+pHhEREREREZHcTAGa5DqnT8Mzz9jbNSdNsleg5VqrVkHbtrB/v902tHPna1p1\ntvvEbrov6c7cXXNpUrYJC1svpFJgpSzNPZmRwejdu3l7716CPDz4tFIlWhctipu2YoqIiIiIiMgN\nTgGa5Cr799udNnftgnnz7MYBuVJyMgwcCBMmQP36sGgRlC9/1ZdLzUhl/NrxjPxlJIW8C/HVE1/x\nZJUns7RdM9OymHHgAINiYkjMzKR/SAh9QkLwybVL+kREREREREScSwGa5Brbt9uBmWXZC7dq1XJ1\nRVdp3Tpo0wZ274Y334Tu3a9p/+my6GV0iuhE1LEout/WnaGNh+KX3y9Lc5cfP06PyEi2nz7Nc0WL\nMrpsWUrqnDMRERERERGRc1z9XjGR62jRImjQAIKCYP36XBqepaRAv372gwQEwLZt0KvXVYdne0/u\n5amvn+KeT+8h2DeYbR22Ma7ZuCyFZ38lJfHIb7/R5Ndf8XF3Z31YGJ9UrqzwTEREREREROQCtAJN\ncrypU+1zzh58EL74Ipd22ty0yV51FhkJo0ZB796Q7+o+fumZ6UxcP5FhPw3D19OXTx/9lGeqP5Ol\n7Zon0tMZsXs37+zbRzFPT76sUoWngoKy3JlTRERERERE5EakAE1yrMxMO2eaMAG6dYPx43Nhp820\nNBgxAsaMgZo1YfNmqFbtqi/3c+zPhEeEs+vILjrX7czwu4YT4BVw2XkZDgfTDxxgSEwMKQ4HQ8uU\noWfJknjnul9QERERERERketPAZrkSKdO2Z02FyyAyZOhUydXV3QVtm61O2z+738wdKi9fdPD46ou\ndfDUQXr/0JvPf/uc+iXrs/nlzdQKzto+1iXHjtEzMpKdSUm0DQ5mVNmyFMuf/6rqEBEREREREbkR\nKUCTHGf/fnjoIfjrL5g/Hx54wNUVXaH0dBg9GkaOhCpVYOPGqz60LcORwdSNUxm8YjCe7p582OJD\n2tZqi5u5/PGFu06fpldUFBHHjtHI359NdeoQ5pe15gIiIiIiIiIi8g8FaJKj/PqrHZ6B3WmzZk3X\n1nPFfvvNPuts+3YYMAAGDQJPz6u61Jq4NYQvDGf7oe28UucVRjUZRWHvwpeddzQ9neGxsUzdt48Q\nLy/mVK3Ko4GBOudMRERERERE5CopQJMcIyICWraEChXslWfFi7u6oiuQkQFvvmlv1SxfHtatg1tu\nuapLxZ+Op++yvny07SNuKX4L69uvp26Jupedl+5wMHX/fobHxpJhWYwpV46uJUuS303NdkVERERE\nRESuhQI0yREmT7YbBTz0kN1p08fH1RVdgZ077VVnmzfDq6/CsGFwFWeMZToyeX/L+wz4cQAA7z74\nLi+FvYS726UP+rcsi4VHj9I7Koq/kpNpX6wYr5UtS9GrXPkmIiIiIiIiIudSgCYulZkJPXvCpEnQ\no4e9iCvXNIbMzIS33oLBg6FMGVi9Gm677aoutXHfRsIjwtm0fxMv1nqR15u+TpBP0GXn7Th1ip5R\nUSw9fpwmAQHMrlqVGr6+V1WDiIiIiIiIiFyYAjRxmVOnoFUre+vm1KnQsaOrK7oCf/5pd9hct85O\nAEeMAG/vK77MseRjDPhxANM3T6dG0RqsfnE1t5e6/bLz4tPSGBIby/T9+wn19mZetWo8VKSIzjkT\nERERERERyQYK0MQl9u2D5s3tTpsLFsD997u6oixyOOCdd6B/fyhRAlauhAYNrvwyloOZ22bSd1lf\n0jLTmHDfBMLrhpPP7dIfyVSHg3f27mXE7t24GcP40FDCS5TAU+eciYiIiIiIiGQbBWhy3W3bZp91\n5uZm73qsUcPVFWVRVBS88IIdmnXtCqNHX9VhbdsObiN8YThr967l2RrP8uY9bxLsG3zJOZZlMffI\nEfpERRGbkkKH4sUZVqYMgTrnTERERERERCTbKUCT62rBAnj6aahUye60WayYqyvKAocD3nsP+vSB\nm26CFSvgzjuv+DIJKQkMXjGYKRunUDmwMj+1+YnGZRpfdt62xER6REXx04kT3Fe4MPOqV6dKruqy\nICIiIiIiIpK7KUCT6+bvRgEtWsBnn+WSTpuxsdCuHSxfbh/S9sYbcIWH9FuWxWfbP6PP0j6cSjvF\n2KZj6XZrNzzcPS4572BqKoNiYphx8CCVChQgonp17i9S5BoeRkRERERERESuhgI0yXaZmXZw9s47\n0KsXjB2bCzptWhZ88IHdIKBwYVi6FJo2veLL7Di8g04Rnfhl9y88VfUpxjcbT8mCJS85JyUzk7f3\n7mX0nj14GsM75cvzcrFieOicMxERERERERGXUIAm2Sox0e60uXgxvPsudOjg6oqyIC4O2reHH36w\n/zl+PBQseEWXSExNZPjPw5mwbgKhhUNZ+txSmpa7dABnWRZfx8fTNzqavampdClRgsGlS1PI49Ir\n1UREREREREQkeylAk2yzd6/dLCA6GhYuhHvvdXVFl2FZ8PHH0K2bvU0zIuKK24NalsXs32fT84ee\nHE8+zoi7RtCzfk/y58t/yXmbTp6ke2Qkq0+epHmRIiypUYMKBQpcy9OIiIiIiIiIiJMoQJNssWUL\nNG8O+fLZnTarV3d1RZexfz+88ord5aBNG3j7bShU6Iou8ceRP+i8qDPLopfxSKVHmHDvBEoHlL7k\nnH2pqQyIjuaTQ4eo5uPD0ho1aFq48LU8iYiIiIiIiIg4mQI0cbr58+1Om1Wq2N8HB7u6okuwLPji\nC+jSBfLnh++/t7scXIHTaacZtXIU49aMI8Q/hIWtF/JA+QcuOScpM5NxcXGM3bMHH3d33qtQgXbB\nweTTOWciIiIiIiIiOY4CNHEay/qn0+Yjj9idNnP0LsRDh+xD2ebOhdat7eKvoMulZVnM3TWX7ku6\nc+jUIQY2HEjfO/rilc/ronMclsWsw4fpFx3N4bQ0upcsyYDSpfHPp4+iiIiIiIiISE6lP7WLU2Rk\nQPfuMGUK9O5td9rM0YupZs+G8HC7yDlz4LHHrmh61LEouizqwqLIRTxQ/gGWP7+c0MKhl5yzNiGB\nHpGRrE9M5LHAQN4IDSXU2/tankJERERERERErgMFaHLNEhOhZUu7aeW0afDyy66u6BLi46FTJ/j6\na3jiCZg6FYKCsjw9OT2Z11e9ztjVYwn2DWZuy7m0qNgCY8xF5+xJSaFfdDSzDh+mtq8vP9WqReOA\nAGc8jYiIiIiIiIhcBwrQ5JrExdmdNmNj7aaVzZq5uqJL+PZbe8tmZiZ8+aWd+l2BhX8upOvirsQl\nxNHn9j4MbDSQAjkqW/QAACAASURBVB4X36N6KiODsXFxjIuLIyBfPj6sWJE2wcG4XyJsExERERER\nEZGcRwGaXLXNm+1Om56edqfNatVcXdFFHDtmNwn44gt4+GF4770r6mwQeyKW7ou78/0f39O0XFMi\nWkdQMbDiRcc7LItPDh5kQEwMx9LT6VWqFP1CQvDTOWciIiIiIiIiuZL+RC9X5fvv7XP3q1aFefNy\ncKfNBQvgpZcgJQU+/RSeeQayuAIsNSOVcWvGMWrlKAp7F2b2E7N5osoTl9yuufLECXpERrL51Cla\nBgUxNjSU0l4XbyogIiIiIiIiIjmfAjS5IpYFEyZAr172ufuffJJDO22eOGF3Nfj4Y3jgAXj/fShe\nPMvTl0YtpfOizkQfj6b7rd0Z0ngIfvn9Ljo+JjmZV6Oj+SY+nrp+fqyqXZsG/v7OeBIRERERERER\ncTEFaJJlGRnQtSu8+y68+iqMGZNDO20uXgzt29vdDWbMgLZts7zqbO/JvfRc0pOv//c1jUs35tun\nvqXqTVUvOv5kRgajd+/m7b17CfLw4NNKlWhdtChuOudMREREREREJM9QgCZZcvKkfeb+0qUwfbq9\nKzLHOXnSXhr3wQd2N4MPPoBSpbI0NT0znQnrJjD85+H4evry2aOf0bp664tu18y0LGYcOMCgmBgS\nMzPpHxJCn5AQfNzdnflEIiIiIiIiIpIDKECTy9qzx+60uXu3vbiraVNXV3QBy5ZBu3Z2w4Bp0+yE\nL4urwH6K/YlOEZ3YdWQXnet25rW7XsPf6+LbL5cfP06PyEi2nz7Ns0WLMqZsWUrqnDMRERERERGR\nPEsBmlzSpk12p838+WHNGrtpQI5y6pS9n/Tdd+Guu+Dnn6FMmSxNPZB4gD5L+/D5b59ze6nb2fLy\nFmoG17zo+L+SkugTFcX3R49Sv2BB1oeFUa9gQSc9iIiIiIiIiIjkVArQ5KLmzrU7bdaoYXfdLFrU\n1RX9x88/wwsvwKFDMHkydOyYpUPZMhwZTNkwhSE/DcHT3ZMZLWbQplYb3MyF555IT2fE7t28s28f\nxTw9+bJKFZ4KCrpkN04RERERERERyTsUoMl5LAveegv69IHHH7c7bXp7u7qqf0lKgv79YdIkaNjQ\nPpgtNDRLU1fvWU14RDi/HfqNV+q8wqgmoyjsXfiCYzMcDqYfOMCQmBhSHA6GlilDz5Il8dY5ZyIi\nIiIiIiI3FAVoco6MDOjc2T5GrF8/GDUqh3XaXL3a7qq5dy+8/bbdFjQLBR4+fZi+y/oyc9tM6hav\ny4aXNnBL8VsuOn7JsWP0jIxkZ1ISbYODGVW2LMXy53fig4iIiIiIiIhIbqEATc46eRKeegp+/NFu\nYNmunasr+pfkZBg82F4ad9ttsHAhVKhw2WmZjkymb57OgOUDMBjee/A92oe1x93twqvIdp0+Ta+o\nKCKOHaORvz+b6tQhzM/P2U8jIiIiIiIiIrmIAjQB7A6bDz0EcXF2p80mTVxd0b+sX2+vOouJgbFj\noWdPyMI2yo37NhIeEc6m/ZtoV7sdY5qMIcgn6IJjj6anMzw2lqn79hHi5cWcqlV5NDBQ55yJiIiI\niIiIiAI0gY0b7U6b3t52p80qVVxd0RmpqTBsGLzxBtSpA1u2ZKm4o0lHGfDjAN7f8j41g2uy5sU1\n1C9V/4Jj0x0Opu7fz/DYWDIsizHlytG1ZEny56h9qyIiIiIiIiLiSgrQbnDffgvPPgs1a9qdNm+6\nydUVnbF5M7RpA3/+CSNGwKuvQr5L/8/VYTn4aOtH9F3WlwxHBpPun0SHWzqQz+38eZZlsfDoUXpH\nRfFXcjLtixXjtbJlKerpmV1PJCIiIiIiIiK5lAK0G5Rlwfjxdi715JMwc2YO6bSZlgYjR8Lo0VCj\nhh2kVa9+2WlbD2wlPCKcdXvX8VyN53jznjcp6lv0gmN3nDpFz6golh4/TpOAAGZXrUoNX19nP4mI\niIiIiIiI5BEK0G5A6el2p83p02HAAHuBV47Ysfjrr/aqs99/txsGDBgAHh6XnHIi5QSDlw9m6qap\nVA6szM9tf6ZR6UYXHBuflsaQ2Fim799PqLc331erRvMiRXTOmYiIiIiIiIhckgK0G0xCgr3ibMUK\nmDEDXnjB1RVhJ3qvvw6vvQaVK8OGDVC79iWnWJbFp9s/pc/SPiSlJ/HmPW/SpV4XPNzPD9xSHQ7e\n2buXEbt342YM40NDCS9RAs8ckRqKiIiIiIiISE6nAO0GEhtrd9rctw+WLIG773Z1RcCOHXaHzW3b\noF8/GDIELnMO2Y7DOwhfGM7KPStpWbUl45uNp0TBEueNsyyLuUeO0CcqitiUFDoUL86wMmUI1Dln\nIiIiIiIiInIFsm0JjjGmkzEmxhiTbIxZZ4ype4mxjxpjfjDGHDbGJBhj1hhjmmVXbTeiDRvg1lsh\nKQnWrs0B4VlGhr3qrE6df4oaOfKS4VliaiK9lvSi1nu1OHz6MEufW8qXT3x5wfBsW2Iid//6K4/9\n/jvlCxRge926TK5QQeGZiIiIiIiIiFyxbAnQjDEtgfHAUKA28CuwxBgTeJEpjYAfgPuBMGAFMN8Y\nUzM76rvRzJkDjRtDaCisXw+VKrm4oF274I47YOBA6N4dtmyBuhfNV7Esiy93fEmlKZV4b/N7jLx7\nJNs7bqdpuabnjT2Ymkr7XbsI27yZQ2lpRFSvzqIaNaji45OdTyQiIiIiIiIieVh2beHsAUyzLOsT\nAGNMB+BB4EXgjf8Otiyrx39eGmiMeRhojh2+yVWwLHjzTejbF1q2tDttenm5sKDMTJgwwQ7OSpeG\nVaugfv1LTtl1ZBedIzrzY8yPPFrpUSbcN4EQ/5DzxqVkZvL23r2M3rMHT2OYdPPNvFK8OB4650xE\nRERERERErpHTAzRjjAdQBxj992uWZVnGmGXApdOSf65hAD/gmLPru1Gkp0N4OHzwgZ1Xvfaaiztt\n/vWX3bFgzRp71dmoUeDtfdHhp9NOM/KXkYxfO54Q/xAiWkdwf/n7zxtnWRZfx8fTNzqavampdClR\ngsGlS1PoMt07RURERERERESyKjtWoAUC7sCh/7x+CKiYxWv0AXyA2U6s64Zx4oTdafPnn+Gjj+wz\n+l3G4YDJk+0GAcWL20U1bHjR4ZZl8d2u7+i+uDvxSfEMajSIVxu8ile+85fObTp5ku6Rkaw+eZLm\nRYqwpEYNKhQokJ1PIyIiIiIiIiI3oBzXhdMY0xoYDLSwLOvI5cb36NEDf3//c15r1aoVrVq1yqYK\nc7aYGLvT5v798MMPcOedLiwmOhpefNEOzTp3tpsGXOIssshjkXRZ1IXFkYt5sPyDTLp/EuUKlTtv\n3L7UVAZER/PJoUNU8/FhaY0aNC1cODufRERERERERERyiFmzZjFr1qxzXktISMjWexrLspx7QXsL\nZxLwuGVZ8/71+kzA37KsRy8x92ngA+AJy7IWX+Y+YcDmzZs3ExYW5pTac7v166FFC/D1hYgIqJjV\n9X7O5nDAtGnQpw8EBcGMGXDXXRcdnpyezOurXmfs6rEE+wYz6f5JNK/QHHsn7z+SMjMZFxfH2D17\n8HF3Z0TZsrQLDiafzjkTERERERERuaFt2bKFOnXqANSxLGuLs6/v9BVolmWlG2M2A02AeXD2TLMm\nwKSLzTPGtMIOz1peLjyT833zDTz3HISFwdy5dm7lErt3Q7t28OOP8MordhcDP7+LDl/w5wK6LurK\nvsR99Lm9DwMaDqCAx7nbMB2WxazDh+kXHc3htDS6lSzJwNKl8c+X4xZQioiIiIiIiEgelF0JxFvA\nzDNB2gbsrpwFgJkAxpgxQHHLstqc+bn1mfe6AhuNMUXPXCfZsqyT2VRjnmBZMHYs9O8PrVrZi71c\n0mnTsuDDD6FnT/D3hyVLoFmziw6PPRFLt8XdmPfHPO4pdw+Ln11MhSIVzhu3NiGBHpGRrE9M5LHA\nQN4IDSX0Es0HREREREREREScLVsCNMuyZhtjAoHXgKLANuBey7LizwwJBkr9a8pL2I0Hppz5+tvH\nwIvZUWNekJ4OHTvaudXgwTB8OPxn1+P1sXcvvPQSLF5sn3n21lt2iHYBqRmpjFszjlErR1HYuzBf\nP/k1j1d+/LztmntSUugXHc2sw4ep7evLT7Vq0Tgg4Ho8jYiIiIiIiIjIObJtD5xlWVOBqRd574X/\n/HzxA7Lkgk6cgMcfh5Ur4eOP4fnnXVCEZcEnn0C3bnZzgIUL4YEHLjr8h6gf6BzRmZgTMfS4rQdD\nGg/B19P3nDGnMjIYGxfHuLg4AvLl48OKFWkTHIy7S5JBEREREREREZEc2IVTLi8mBh58EA4ehKVL\noXFjFxRx4IB9xtn8+fbhaxMnQqFCFxy69+ReeizpwTf/+4Y7y9zJdy2/o+pNVc8Z47AsPjl4kAEx\nMRxLT6dXqVL0CwnBT+eciYiIiIiIiIiLKZ3IZdauhYcfhoIF7e+ve6dNy4JZs6BzZ/D0tDsWPPzw\nBYemZaYxYd0EXvv5Nfzy+/H5Y5/Tqlqr87Zrrjxxgh6RkWw+dYqWQUG8Xq4cZXTOmYiIiIiIiIjk\nEArQcpHZs+2tmnXrwnffQWDgdS7g8GHo0MG++dNPw+TJUKTIBYeuiFlBp4hO/Hn0T7rU68KwO4fh\n73XuuWgxycm8Gh3NN/Hx1PXzY1Xt2jS4yNlpIiIiIiIiIiKuogAtF7AsGDMGBg6E1q3tTpv581/n\nIr7+GsLD//n+iScuOOxA4gF6/dCLWTtm0aBUAza/vJmawTXPGXMyI4PRu3fz9t69BHl48GmlSrQu\nWhQ3nXMmIiIiIiIiIjmQArQcLi3NXvT10UcwdKj9dV1zpiNH7O2aX30Fjz0G774LN9103rAMRwaT\nN0xmyIoheOXz4qOHP+L5ms/jZtzOjsm0LGYcOMCgmBgSMzPpHxJCn5AQfNzdr+MDiYiIiIiIiIhc\nGQVoOdjx43anzVWr7GaXzz13nQuYO9duFJCRYZ971rLlBdO71XtWEx4Rzm+HfqPjLR0ZefdICnmf\n21Bg+fHj9IiMZPvp0zxbtChjypalpJfX9XoSEREREREREZGrpgAth4qOtjttHj4My5ZBo0bX8ebH\njkHXrvD559CiBUybBsHB5w07fPowry59lY9//Zh6Jeqx8aWN1Cle55wxfyUl0Scqiu+PHqV+wYKs\nDwujXsGC1+tJRERERERERESumQK0HGjNGruxZUAArFsH5ctfx5svXAgvvQRJSfayt2efPW/VWaYj\nk2mbpzFw+UDcjBvTHppG+7D252zXPJGezojdu3ln3z6KeXryZZUqPBUUdF4HThERERERERGRnE4B\nWg7z1VfQpg3Uq2c3u7xIk0vnS0iAHj3sw9buvx/efx9KlDhv2IZ9GwhfGM7mA5tpV7sdrzd9ncAC\n/7QDzXA4mH7gAENiYkhxOBhapgw9S5bEW+eciYiIiIiIiEgupQAth/h3p81nn4UPPriOnTaXLIH2\n7e0Q7cMP4YUXzlt1djTpKP1/7M8HWz6gVnAt1rZby20lbzv3MseO0TMykp1JSbQNDmZU2bIUu+7t\nQkVEREREREREnEsBWg6Qlmaf1T9zJgwbBkOGXKdOm4mJ0KuXvdqsaVM7PAsJOWeIw3IwY+sM+i3r\nR4Yjg0n3T6LjLR1xd/tnRdmu06fpFRVFxLFjNPL3Z1OdOoT5+V2HBxARERERERERyX4K0Fzs706b\nq1fDZ5/BM89cpxsvXw4vvghHjsB778HLL5+X2m09sJXwiHDW7V3H8zWf542mb1DUt+jZ94+mpzM8\nNpap+/YR4uXFN1Wr8lhgoM45ExEREREREZE8RQGaC0VF2Z02jxyxO202bHgdbnrqFPTrB1OmwJ13\nwooVULbsOUNOpJxg8PLBTN00lSpBVfil7S80LP1PcekOB1P372d4bCwZlsWYcuXoWrIk+d3cEBER\nERERERHJaxSgucjq1fDII1CokN1p8+abr8NNf/nFPt/s4EF45x0ID4d/hV6WZfHp9k/ps7QPSelJ\njLtnHJ3rdcbD3ePs+wuPHqV3VBR/JSfTvlgxXitblqKenteheBERERERERER11CA5gKzZtk51q23\nwrffXodOm0lJMGAATJoEt99uNw34T2L326HfCI8IZ9WeVTxd7WnGNxtPcb/iZ9/fceoUPaOiWHr8\nOE0CAphdtSo1fH2zuXAREREREREREddTgHYdWRaMGgWDB8Nzz9ln92d7k8o1a6BtW4iLg3HjoFs3\ncP+nAcDJ1JMM+2kYk9ZPonyR8ix7bhlNyjU5+358WhpDYmOZvn8/od7efF+tGs2LFNE5ZyIiIiIi\nIiJyw1CAdp2kpdnn9H/8Mbz2GgwalM2dNlNS7Hae48dD3bowbx5UqnT2bcuy+Or3r+i5pCcJqQmM\nunsUPer3wNPd3o6Z6nDwzt69jNi9GzdjGBcaSqcSJfDUOWciIiIiIiIicoNRgHYdHDsGjz0Ga9fC\nF19Aq1bZfMMNG6BNG4iOhjFjoFevc1ad7YzfSedFnVkes5zHKj/G2/e+TYh/CGAHa3OPHKFPVBSx\nKSl0KF6cYWXKEKhzzkRERERERETkBqUALZtFRtqdNo8eheXLoUGDbLxZaqq9vO3116F2bdiyBapW\nPfv26bTTjPhlBG+tfYsQ/xAWPbOI+26+7+z72xIT6REVxU8nTnBf4cLMq16dKj4+2ViwiIiIiIiI\niEjOpwAtG61aZXfaLFLkOnTa3LLFXnX2xx8wfDj07Qse/3TP/Hbnt/RY0oP4pHgGNxpMnwZ98Mrn\nBcDB1FQGxcQw4+BBKhUoQET16tyf7Z0NRERERERERERyBwVo2eSLL+xOm7ffDnPmQOHC2XSjtDS7\nM8GoUVC9OmzcCDVrnn37r6N/0WVRF5ZELeGhCg8x8b6JlCtUDoCUzEze3ruX0Xv24GkMk26+mVeK\nF8dD55yJiIiIiIiIiJylAM3JLAtGjIChQ+0FYdOnQ7YdH7Z9u32THTvsrgQDBpy9WXJ6MmNWjWHs\n6rEU9yvOvKfn0bxi8zM1WnwdH0/f6Gj2pqbSuUQJhpQuTaEzK9ZEREREREREROQfCtCcKDUVXnoJ\nPv0URo6086xs6bSZkQFjx9pbNStWhPXrISzs7Nvz/5hP18Vd2Z+4n1dvf5X+DftTwKMAAJtOnqR7\nZCSrT56keZEiLKlRgwoFCmRDkSIiIiIiIiIieYMCNCc5etTutLl+PcyaBU8/nU03+v13aNvWPvOs\nXz8YMgTy5wcg5ngM3RZ3Y/6f82kW2owfnv2B8kXKA7AvNZUB0dF8cugQ1Xx8WFqjBk2zbV+piIiI\niIiIiEjeoQDNCf76y+60efy43Wnz9tuz4SaZmTBunB2YlSsHa9dCvXoApGak8uaaNxm1chSBBQL5\n5slveKzyYxhjSMrMZFxcHGP37MHH3Z33KlSgXXAw+XTOmYiIiIiIiIhIlihAu0YrV9qdNoOC7E6b\noaHZcJM//rBXna1fD7162YesedkdNJdELqHzos7Enoil5209Gdx4ML6evjgsiy8OHaJfdDSH0tLo\nXrIkA0uXxj+f/pWLiIiIiIiIiFwJpSnX4LPPoF07aNDA7rRZqJCTb5CZCZMm2YeplSoFq1adXd4W\nlxBHjyU9mLNzDneWuZPvn/6eKkFVAFibkECPyEjWJybyWGAgb4SGEurt7eTiRERERERERERuDArQ\nroJl2ef3Dx8OL7wA772XDZ02IyPti69eDd26wahRUKAAaZlpTFg3gdd+fo2C+QvyxWNf8HS1pzHG\nsCclhX7R0cw6fJjavr78VKsWjQMCnFyYiIiIiIiIiMiNRQHaFUpNhfbt7dVno0fb5/g7tdOmwwFT\npkDfvlCsGPz0EzRqBMCKmBV0iujEn0f/pEu9Lgy/azgF8xfkVEYGY+PiGBcXR0C+fHxYsSJtgoNx\nz5YWoCIiIiIiIiIiNxYFaFfg6FF49FHYsAG+/BJatnTyDWJi4MUX7dAsPBzGjgVfXw4kHqDXD72Y\ntWMWd4TcwZYntlCjaA0clsXMAwcYEBPDsfR0epUqRb+QEPx0zpmIiIiIiIiIiNMoacmiP/+0O20m\nJMCKFVC/vhMvblkwfTr07g2FC8OyZdCkCRmODCavm8CQFUPwyufFzIdn8nzN5zHGsPLECXpERrL5\n1ClaBgXxerlylNE5ZyIiIiIiIiIiTqcALQt++cVeeXbTTXanzXLlnHjxPXvsTgTLlsHLL8Obb0LB\ngqzas4rwheHsOLyDjrd0ZOTdIynkXYiY5GRejY7mm/h46vr5sap2bRr4+zuxIBERERERERER+Tc3\nVxeQ0336KTRtCrVqwdq1TgzPLAtmzIDq1WHnTli8GKZN45BbMm3ntqXhRw3x9vBm40sbmfLgFNw9\n/OgXFUWlDRtYm5DAp5UqsS4sTOGZiIiIiIiIiEg20wq0i7AsGDYMXnvNPpbs3Xed2Glz3z57tVlE\nBLRtC2+/TWZBP97bMIWBywfi7ubO9Iem0y6sHRaG9/fvZ1BMDImZmfQPCaFPSAg+7u5OKkZERERE\nRERERC5FAdoFpKTYuyq/+ALGjLEbYjqloaVl2e07u3YFb2+YPx8eeoj1e9cTPjucLQe28FLYS4xu\nMprAAoEsP36cHpGRbD99mmeLFmVM2bKU9PJyQiEiIiIiIiIiIpJVCtD+48gReOQR2LwZZs+GJ590\n0oUPHoRXXoF58+DZZ2HiRI56WfSb9xIfbP2A2sG1WdduHbeWvJW/kpJo/9tvfH/0KPULFmR9WBj1\nChZ0UiEiIiIiIiIiInIlFKD9yx9/2J02ExPtTpu33eaEi1oWfPUVdOoE+fLBt9/ieORhPtzyIf1+\n7EemI5PJ90+mwy0dSMx00Csyknf27aOYpyezKlem5U03YZyy/E1ERERERERERK6GArQzfvoJHnsM\ngoPtTptlyzrhoocPQ3g4zJkDTz0FU6awJX0P4R/ezvp962lTsw1v3PMGhb0DmXbgAENiYkhxOBha\npgw9S5bEW+eciYiIiIiIiIi4nAI04JNPoH17aNQIvvkGAgKccNFvvoGOHc+uQDve/B4GLR/Eu5ve\npdpN1fil7S80LN2QJceO0fP3TexMSqJtcDCjypalWP78TihAREREREREREScwc3VBbiSZcHgwdCm\njf21aJETwrOjR6FVK/vwtIYNsXbs4OMKyVScXJFPt3/K+Gbj2fLKFoICw3hw+3bu276dQA8PNtWp\nw4xKlRSeiYiIiIiIiIjkMDfsCrSUFHjhBfjySxg7Fvr0cUKnzXnz4OWXIS0NPv+c7XdXpdOiJ1m1\nZxWtqrViXLNx5PcKomdUDFP37SPEy4tvqlblscBAnXMmIiIiIiIiIpJD3ZAr0OLjoUkTmDvX3mn5\n6qvXGJ4dPw7PPw8PPwx165K4ZR09Cm8kbHodjiQd4cfnf+TjRz/j6wQH5devZ+bBg4wuV47/1a3L\n40FBCs9ERERERERERHKwG24F2q5ddqfNU6fsxgG33nqNF4yIgJdegtOnsT76iC/DPOk1504SUhMY\ndfcout/WnaUnEqm+cSN/JSfTvlgxXitblqKens54HBERERERERERyWY31Aq0FSugfn3w8oL1668x\nPEtIgHbt7DSuenX++vk7mphPaP3dM9xe6nZ2ddrFg7U60fz3XTTfsYOS+fOz9ZZbmFaxosIzERER\nEREREZFc5IZZgTZzpr1Q7K674Ouvwd//Gi62dKkdnp04Qcq77zC03B7emteMMgFlWPzMYsJC7mJI\nbCzT90cR6u3N99Wq0bxIEW3VFBERERERERHJhfL8CjSHAwYOtBsGvPACLFx4DeFZYiJ06ADNmmFV\nqMCib9+gfPJYJm18h6GNh7LplV/5zbMyN69fz6xDhxgXGsqOunVpoSYBIiIiIiIiIiK5Vp5egZac\nbIdmX30Fb7wBvXtfQ7OAFSvgxRchPp7Dbwzj+eA1LFndkeYVmjPh3gn8mulHnS3biU1JoUPx4gwr\nU4ZAbdUUEREREREREcn18myAFh9vN8XcutXutPn441d5odOnoV8/mDyZzEYNmTjiQfrHjqb4seLM\ne3oepYrfSbuoKH46sYf7ChdmXvXqVPHxceqziIiIiIiIiIiI6+TJAG3nTvts/6Qk+PlnqFfvKi+0\ncqW9hG3/fnYMeIkWgT+wL3Y9fRv05YV6vRgVd4AZmzdTqUABIqpX5/4iRZz6HCIiIiIiIiIi4np5\n7gy05cvtTpsFCtidNq8qPEtOhp49oXFjUooE8PLIW6nu+T4Vgiqx+ZXf8C7XjhpbtvPdkSNMuvlm\nfr3lFoVnIiIiIiIiIiJ5VJ5agTZjBrzyCtx9N8yefZXNAtatgzZtsHbvZlmHZjxS7CcKmyC+fuIb\nHEENaR4Vzd7UVDqXKMGQ0qUp5OHh9OcQEREREREREZGcI0+sQHM4YMAAaNfO/lqw4CrCs5QU6NsX\nGjTghJfhvt5FeaDYj3Sp350v2mxmYlo5Wv7vf1T38WFH3bq8ffPNCs9ERERERERERG4AuX4F2oMP\ndsDX934iI3szbpwfPXteRafNjRuhbVusyEg+b1mZtjf/TqPQu1h6z0I+SnCn0fb/Uc3Hh6U1atC0\ncOFseQ4REREREREREcmZcn2AdvDgu0A8pUo9zssvz8EYv6xPTk2FESOwXn+dQ6HBNO/gxr6QY3zY\nbBaxvrfyYGQcPu7uvFehAu2Cg8nnlicW7ImIiIiIiIiIyBXI9QEaGOA+9u2zGDRoPBMnDsvatK1b\noU0bHDv/x+T7CvFqnQN0vL0bVat2ZVDcAQ4d20P3kiUZWLo0/vnywC+TiIiIiIiIiIhclTyzpMrh\nuI9581ZffmB6OgwfjlWvHrtPxhHWLpOvH6/EB203sjbwGV6KjKWenx8769XjjdBQhWciIiIiIiIi\nIje4PJQOGdLTC2BZFuZih6D99htWm+extm/njUbuTG7mQa97P2Nj/to8t/swtX19WVGzJncWKnR9\nSxcRERERv3o/OwAAIABJREFUERERkRwrDwVoFh4epy8cnmVkwBtv4Bg2lOhAd1q1t6jdoiOtyndg\nwIF4ApJP8GHFirQJDsb9ijsQiIiIiIiIiIhIXpZnAjQ3t8W0aHHH+W/s3En6c61x3/orY2+3WNg6\njAfvmsj0Yw6O7T9Mr1Kl6BcSgp+2aoqIiIiIiIiIyAXkgdTIws1tEZUrv83IkXP+eTkzE8f4cTgG\nDyLG30GXDn7UbDOFFLdKDD94ipZBQbxerhxlvL1dV7qIiIiIiIiIiOR4uT5AK1YsnCefvJ+RI+fg\n5+dnv/jnnyS2fhyfLTuYeBts690Fr9C2vHn8JHX9DKtq16aBv79L6xYRERERERERkdwh1wdoCxa8\nS1hYmP2Dw8GpcaPxHDyMg76ZDO9XA7enpjA7IZOg0yl8UqkSzxQtipvOORMRERERERERkSzK9QHa\nY7fWpXSNasyaNBXTpS3FtkYytYEXa0fNYGm+UBJPZtI/JIQ+ISH4uLu7ulwREREREREREcllcn2A\nNifDwcEt23n+jjt4uyAMmfQya+q15X/JqTxbuDBjypalpJeXq8sUEREREREREZFcys3VBVyr5oGB\nLPHxoUWxYjR+7S0+qN4Kf4/8rA8L49PKlRWeibjIrFmzXF2CiFyCPqMiOZc+nyI5mz6jIjembAvQ\njDGdjDExxphkY8w6Y0zdS4wNNsZ8boz5wxiTaYx5K6v3OTByJJMHDaJbsWKc9C/ErMqVWV27NvUK\nFnTOg4jIVdF/WIjkbPqMiuRc+nyK5Gz6jIrcmLIlQDPGtATGA0OB2sCvwBJjTOBFpuQHDgMjgG1X\neDOs227DPPEEhXt056mgIIyaBIiIiIiIiIiIiJNk1wq0HsA0y7I+sSxrF9ABSAJevNBgy7J2W5bV\nw7Ksz4CTV3ND67bbOO7jg5tbrt+VKiIiIiIiIiIiOYjT0yZjjAdQB/jx79csy7KAZUB9Z9/vXzfG\nLaAQ9q1EREREREREREScIzu6cAYC7sCh/7x+CKjoxPvY3QH27LF/siwCjGHr1q1OvIWIXK2EhAS2\nbNni6jJE5CL0GRXJufT5FMnZ9BkVyZl27tz597fZ0k3SOHvFljGmGLAPqG9Z1vp/vT4WaGRZ1iVX\noRljVgBbLcvqeZlxrYHPnVCyiIiIiIiIiIjkDc9YlvWFsy+aHSvQjgCZQNH/vF4UOOjE+ywBngFi\ngRQnXldERERERERERHIXL6AMdl7kdE4P0CzLSjfGbAaaAPMAjN0WswkwyYn3OQo4PVEUERERERER\nEZFcaU12XTg7VqABvAXMPBOkbcDuylkAmAlgjBkDFLcsq83fE4wxNQED+AJBZ35OsyxrJyIiIiIi\nIiIiIi6SLQGaZVmzjTGBwGvYWze3AfdalhV/ZkgwUOo/07YCfx/IFga0BnYD5bKjRhERERERERER\nkaxwehMBERERERERERGRvMTN1QWIiIiIiIiIiIjkZArQRERERERERERELiFXBmjGmE7GmBhjTLIx\nZp0xpq6raxIRMMY0NMbMM8bsM8Y4jDEtXF2TiNiMMf2NMRuMMSeNMYeMMd8ZYyq4ui4RsRljOhhj\nfjXGJJz5WmOMuc/VdYnI+Ywx/c78t+5brq5FRMAYM/TMZ/LfX/9z9n1yXYBmjGkJjAeGArWBX4El\nZ5oWiIhr+WA3DQnnn6YgIpIzNATeAW4FmgIewA/GGG+XViUif4sD+mI306oDLAe+N8ZUdmlVInKO\nM4s3Xsb+c6iI5Bw7sJtYBp/5usPZN8h1TQSMMeuA9ZZldTvzs8H+D45JlmW94dLiROQsY4wDeMSy\nrHmurkVEznfmL54OA40sy1rl6npE5HzGmKNAb8uyPnJ1LSICxhhfYDPQERgMbLUsq6drqxIRY8xQ\n4GHLssKy8z65agWaMcYD+2/kfvz7NctOAJcB9V1Vl4iISC4UgL1S9JirCxGRcxlj3IwxTwMFgLWu\nrkdEzpoCzLcsa7mrCxGR85Q/c5RQlDHmM2NMKWffIJ+zL5jNAgF34NB/Xj8EVLz+5YiIiOQ+Z1Zv\nTwBWWZbl9PMhROTqGGOqYQdmXkAi8KhlWbtcW5WIAJwJtWsBt7i6FhE5zzqgLfAHUAwYBvxijKlm\nWdZpZ90ktwVoIiIicu2mAlWABq4uRETOsQuoCfgDTwCfGGMaKUQTcS1jTEnsv3hqallWuqvrEZFz\nWZa15F8/7jDGbAB2A08BTjsGIbcFaEeATOyD4f6tKHDw+pcjIiKSuxhjJgMPAA0tyzrg6npE5B+W\nZWUA0Wd+3GqMqQd0wz5vSURcpw4QBGw5s4ob7J1RjYwxnYH8Vm47XFwkD7MsK8EY8ydwszOvm6vO\nQDuT9m8Gmvz92pnfwJoAa1xVl4iISG5wJjx7GLjLsqw9rq5HRC7LDcjv6iJEhGVAdewtnDXPfG0C\nPgNqKjwTyVnONPy4GXDqXxbnthVoAG8BM40xm4ENQA/sA1ZnurIoEQFjjA/2b1R//81cOWNMTeCY\nZVlxrqtMRIwxU4FWQAvgtDHm79XcCZZlpbiuMhEBMMaMBhYBewA/4BmgMdDMlXWJCJw5Q+mcM0ON\nMaeBo5Zl7XRNVSLyN2PMm8B87G2bJYDhQDowy5n3yXUBmmVZs40xgcBr2Fs3twH3WpYV79rKRAT7\nUNUV2J39LGD8mdc/Bl50VVEiAkAH7M/lT/95/QXgk+tejYj8103Y/39ZDEgAtgPN1O1PJMfSqjOR\nnKMk8AVQBIgHVgG3WZZ11Jk3MVptKiIiIiIiIiIicnG56gw0ERERERERERGR600BmoiIiIiIiIiI\nyCUoQBMREREREREREbkEBWgiIiIiIiIiIiKXoABNRERERERERETkEhSgiYiIiIiIiIiIXIICNBER\nERERERERkUtQgCYiIiIiIiIiInIJCtBEREREREREREQuQQGaiIiIyA3IGOMwxrRwdR0iIiIiuYEC\nNBEREZHrzBjz0ZkAK/PMP//+PsLVtYmIiIjI+fK5ugARERGRG9QioC1g/vVaqmtKEREREZFL0Qo0\nEREREddItSwr3rKsw//6SoCz2ys7GGMijDFJxpgoY8zj/55sjKlmjPnxzPtHjDHTjDE+/xnzojFm\nhzEmxRizzxgz6T81BBljvjXGnDbG/GmMaZ7NzywiIiKSKylAExER+T97dx4fVXX/f/x1JgnZSUIC\nIWHJAoIBCRZUQFFEVBABbQUrioJ+W9eiotX2V7BQBRELIiqu/da91iJVUYtaS7VfLahAKy5xgZAg\nsoYtC4QkM+f3x53JzGQjK0ng/Xw85pGbc88999yZO0nmk885R6RtuhtYBmQDLwJ/Nsb0BTDGRAHv\nAHuAwcBE4FzgYd/BxpgbgEeAx4H+wIXAt1XO8Vvgz8AA4G/Ai8aY+Ja7JBEREZH2yVhrW7sPIiIi\nIscVY8zTwBSgNKDYAvdaa+8zxniAR621vwg4ZjWwzlr7C2PMz4H5QHdrbal3/wXAG0CKtXa3MWYr\n8L/W2tm19MED3G2tneP9PgooBsZYa99t5ksWERERadc0B5qIiIhI61gFXE/wHGh7A7bXVKm/Ghjo\n3T4R+MwXPPP6CGd0QV9jDECq9xx1+dy3Ya09aIwpBLrU9wJEREREjhcKoImIiIi0jhJr7eYWavtQ\nPeuVV/neoik+RERERKrRH0giIiIibdPQGr7P8W7nAAONMZEB+4cDbuBra20xkAeMaulOioiIiBwP\nlIEmIiIi0jrCjTHJVcoqrLV7vNuTjDHrgA9x5ks7FbjGu+9FYA7wrDHmdzjDLh8CnrPWFnjrzAEe\nM8bsBlYCHYHTrbWPtND1iIiIiByzFEATERERaR1jgG1Vyr4B+nm3ZwOXAUuB7cBl1tqvAay1h4wx\no4ElwCfAQeAV4HZfQ9ba54wx4cAM4PdAgbdOZZUa+qTVpURERERqoFU4RURERNoY7wqZF1trV7R2\nX0REREREc6CJiIiIiIiIiIjUSQE0ERERkbZHQwRERERE2hAN4RQREREREREREamDMtBERERERERE\nRETqoACaiIiIiIiIiIhIHRRAExERERERERERqYMCaCIiIiIiIiIiInVQAE1ERERERERERKQOCqCJ\niIiIiIiIiIjUQQE0EREROSYZY7YaY54M+H6UMcZjjDm9Hsd+aIx5t5n7M9cYU96cbYqIiIjI0aEA\nmoiIiLQaY8zrxpgSY0x0HXVeNMYcNsYkNLB5W8+y+h57RMaYaGPMbGPM8Fra9DSmXRERERFpXQqg\niYiISGt6EYgAflzTTmNMJDAB+Ju1dl9TTmSt/QcQaa39d1PaOYIYYDZwVg37Znv3i4iIiEg7owCa\niIiItKYVQDFweS37LwaicAJtTWatLWuOdupg6ji3x1qrIZxHYIyJaO0+iIiIiFSlAJqIiIi0Gmtt\nKfBXYJQxJqmGKpcDRcAbvgJjzK+MMR8ZY/YYYw4aYz41xlx8pHPVNgeaMeYGY8wmb1ura5ojzRgT\nboy5xxizzhiz3xhTbIx53xhzZkCdXsA2nKGac73n8hhjfuPdX20ONGNMqHfI5yZjTKkxJtcYc7cx\nJqxKva3GmL8aY84yxnxijDlkjNlojKkt8Fi1//V+zowxV3nPUeKt/74x5pwqdS40xnxgjCk0xhww\nxqwxxlxapb9P1tB20NxyAa/JRGPMvcaYrUCxMSbKGJNojFlkjPncGFPkfd7fMsacVEO7Ed7n7Vvv\n87jNGLPMGJNmHFuMMctqOC7S2/bD9XkeRURE5PilAJqIiIi0theBMODSwELvnGfnA3+11h4O2HUz\nsA6YBfw/nHnFlhtjzq/HuYLmNjPGXAcsBb4H7gBW4wTrUqscFw9MA/4B3AnMAboC7xpj+nvr7ABu\nwslCWwZM8T5eCzh31bnVnsEZ2vkxMAP4P+91vVBDv/sCfwbeBm4DDgDPGmNOqMd11+s5M8bc4+3T\nIeAu73VuBUYG1PkZznPUEbgX+BXwGTC6Sn9rUlv5HOA84H5gJlAO9AYuBF7HeW5+DwwE3jfGdAno\nTwiw0nvcGuBW4EEgAehnrbU499iFxpjYKuf1ZTg+X0u/RERERAAIbe0OiIiIyHFvFbAdJ9vs0YDy\nS3H+Vqk6fDMzMKBmjFmKE8CZAdR75Uxvltdc4FNglLXW7S3/BngM2BRQfTeQYa2tCDj+KeA74BfA\nDdbaEmPMX3ECcp9Za/90hPMP8l2ztfYX3uLHjDF7gFuMMWdYaz8KOORE4HRr7cfe4/8KbAGuBn5z\nhMs94nNmjOnjbedla+3kgGMfDjguHlgMfIjznDXXkNRQnGurbM8Ys95ae2JgJWPMn4AcnGte4C2+\nBhgB/MJaG3j/3B+w/RxOoG8S8MeA8inARmvtJ810HSIiInKMUgaaiIiItCprrQcns2qYMaZnwK7L\ngZ04AbbA+oGBoHic7LAPgUENPPUQIBF4zBc88/ojzrDRoD76gmfeIYEJOFlzaxtxXp+xOBlZi6uU\nL8LJYruwSvkGX/DM26edOAG8zCOdqJ7P2U+8X++uo6nROBlb85t5Prenq7ZXJZgWYozphPO6bKR6\nv3fgBD1rZK3NwcnAuyKgzSScrLeq2X4iIiIi1SiAJiIiIm3BizhBo8sBjDHdgOHAS94heJWMMRO8\nc24dAvYCu4CfA3ENPGcaTgBrY2ChN3CTV7WyMeZqY8znwGFgj/e8Yxpx3sDzV1hrAzPdsNb+gBMo\nSqtSf0sNbezDGapYp3o+Z5mAG/imjqZ6eb9+eaRzNlBe1QJjjMsYc7sx5jugFCjA6XcWwf3uBXxd\n9T6pwXPAWcYY3/DcnwIhNNMCFSIiInJsUwBNREREWp21dj3wNeAbOuibHD9oGKQxZiTwKk6A6Xrg\nAuBc4GVa8O8aY8w04H9xhg9Ow8nEOhf4oCXPW4W7lvJaV/6EVnvOagtmhdRSfqiGst/izHv2D5z7\n4Xycfn9D4/r9Es7cb7576wpgjbU2txFtiYiIyHFGc6CJiIhIW/EicLcxZgBOIO07a+26KnV+ApQA\nYwKHXXoXA2iofJzg0wk4wxl9bYUB6TjDR30uAb6x1lZd6ODeKm0eKQuq6vlDjTG9ArPQvBlSsd79\nzaG+z9kmnADXicBXtbS1Cec5O4maM+J89uEME60qjfpnr10CvGutvT6w0Dt8dmuVPg00xri8w4Fr\nZK0tMMa8DVzhnT9uKHBDPfsiIiIixzlloImIiEhb4RvGeTdwMjXPTeXGySKqzGQyxmQC4xtxvo9x\nhjNe713J0ednOAGsqucNYow5Azi1SnGJ92tNwaOq/oZzvbdWKb8dJxD3Vj3aqI/6Pmever/ONsbU\nltX2Ds41/sYY06GOc27CmdMu8JwXAyk11K0t6OimSnadMWYykFyl3nKcFVHrEwx7Hmclz/lAGfCX\nehwjIiIiogw0ERERaRustXnGmH8DF+EEVWpaxfIt4GbgHWPMSzgBmRtxhvX1r8dpKgMy1tpyY8xd\nwCPAP40xLwO9gauAqsP63gQmeDOXVuLMu3UdTqZWeECbJcaYb4HJxphcnEysDd5J7Kte73pjzIvA\njcaYROD/gGE4K0P+pcoKnE1Rr+fMWvutMeY+4NfAB8aY13CCTKcC+dba31pr9xtjbseZsP8TY8yf\ngf04Qakwa+3PvM39AbgYeNsYsxzneb2c6s8r1D4E9U2cQN0fgDXec0wGNlep9zRwJfCQMWYY8BEQ\ng7NAwGJr7cqAuiu8/Z0IvGGt3VfbkyYiIiISSBloIiIi0pa8iBM8+7imuamstX/Hmfw+FXgQmIST\nsfVmDW1Zqmc3BX1vrX0M+AXQDWe+rSHAOGBbYF1r7R+AWcCPvOcdBVwG/LeGc1yDsyrkYpwg4I9r\nOz/OfGq/8553MXAmcA9OEO1I11Jbm8E7G/CcWWtn4mTgRQNzgTlAdwJWQrXWPokTHCvGeU7m4wS3\nVgbU+RtwB85w0EXAKThzrwU9r0fo/z04z8kYb78HeLd/IPi1cePMSTcfJwC5GLgFZ6GHoOGi1trA\nrLPnajmviIiISDXmyAsWiYiIiIgcG4wxD+EEKLt6A2oiIiIiR9SoDDRjzE3GmM3GmEPeJdGrzv9R\n23FnGGPKjTHra9g3yRiT423zM2PMBY3pm4iIiIhITYwxUThDSf+i4JmIiIg0RIMDaMaYn+Kk4s/G\nGcbwGc6cGklHOC4OeBZ4r4Z9p+MMcXgKZ9Lg14HXjDH9Gto/EREREZFAxpguxpjLcf7ejAMebuUu\niYiISDvT4CGcxpg1OPOS3OL93gDfAw9Za++v47iXgG9xVoG6yFo7KGDfn4Eoa+2EgLLVwH+stTc2\nqIMiIiIiIgGMMaOAv+PMTTfbWvtUK3dJRERE2pkGZaAZY8KAwcA/fGXWicC9hzNpa23HXQ1k4EyS\nW5NhVM9Me6euNkVERERE6sNa+w9rrctam6rgmYiIiDRGaAPrJwEhwM4q5TuBvjUdYIw5AbgXGG6t\n9TgJa9V0raXNrrV1xLvc+2ggDyitR99FREREREREROTYFAGkA+9Ya/c0d+MNDaA1iDHGhbMc/Wxr\n7SZfcTM1P9rbtoiIiIiIiIiICMAVOPOeNquGBtAKADeQXKU8GWdOiapigVOAk40xS71lLpyp08qA\n862173uPrW+bPnkAL7zwAllZWQ24BBE5GmbMmMHixYtbuxsiUgu9R0XaLr0/Rdo2vUdF2qacnBym\nTJkC3nhRc2tQAM1aW26MWQeMAlZA5SICo4CHajikEDipStlNwEjgEvwXtbqGNs7zltemFCArK4tB\ngwbVUU1EWkNcXJzemyJtmN6jIm2X3p8ibZveoyJtXotM89WYIZwPAM94A2mfADOAKOAZAGPMfCDV\nWjvVu8DAV4EHG2N2AaXW2pyA4iXA+8aY24C3gMk4ixX8vBH9ExERERERERERaTYNDqBZa/9ijEkC\n7sYZZvlfYLS1dre3SlegRwPbXG2MuRyY5318B1xkrf2q7iNFRERERERERERaVqMWEbDWPgo8Wsu+\nq49w7O+A39VQvhxY3pj+iIiIiIiIiIiItBRXa3dARI5NkydPbu0uiEgd9B4Vabv0/hRp2/QeFTk+\nGWeasvbHGDMIWLdu3TpN4CgirWbLli0UFBS0djdEREREWlVSUhI9e/Zs7W6IyHFs/fr1DB48GGCw\ntXZ9c7ffqCGcIiLiBM+ysrI4ePBga3dFREREpFVFRUWRk5OjIJqIHLMUQBMRaaSCggIOHjzICy+8\nQFZWVmt3R0RERKRV5OTkMGXKFAoKChRAE5FjlgJoIiJNlJWVpaHkIiIiIiIixzAtIiAiIiIiIiIi\nIu1SUVERs2++mevHjWvR8ygDTURERERERERE2p2ioiIuGTaM23JymODxcEoLnksZaCIiIiIiIiIi\n0u4snDmT23JyGOPxYFr4XAqgiYiIiIiIiIhIu/PRihWM9niOyrk0hFNERERERERERNoejwe2bYPN\nm51Hbm7lV5ubS/S2bS2eeeajDDQRERFpF5555hlcLhdbtmxp7a5IG/TBBx/gcrn417/+1dpdETmm\n6L0lIi1u3z5Yvx5eeQV+/3u48UYYMwb69oXISOjRA846C6ZOhSeegI0bIS0Nc801lCQmYo9SN5WB\nJiIiIu2CMQZjjtb/GKU90v3RMPPnz6dfv35cdNFFrd0VaeP03hKRJikthby86llkvu0DB/x1Y2Mh\nMxMyMuDCC/3bGRmQng5RUUFNn3HgAO8sXcqYozCMUwE0EZGjyFrbYn+EtmTb0rbpvjp+6bWXprj3\n3nuZNGmSAmhVtPS9r/eWiBxz3G7/MMuqwbHNm519PmFhkJbmBMROOw1++lNn2xco69QJGvAz8pfz\n5nHJqlXYnBy6tHAQTQE0EZEWVlRUxMyZC3njjY8oL48mLKyE8ePPYN68XxIbG9tm267JwYMHiary\nX5+2xu124/F4CAsLa+2utKiioiJm3jOTN957g/KQcsLcYYw/dzzz7prXPPdVC7UtTVdUVMTCmTP5\n6I03iC4vpyQsjDPGj+eX85rntW+ptkXasqKiImbOn88bH3xAeXg4YYcPM37ECOb9v//XLPd+S7cv\nItKirHWGWdYUHNu82ckuKy/3109J8QfEzjknOIusWzcICWm2rsXGxrJ89WoWzZrFymXLYPv2Zmu7\nGmttu3wAgwC7bt06KyLSGtatW2eP9HOosLDQ9u9/nnW5VlrwWOe3j8e6XCtt//7n2cLCwkafvyXb\nttba2bNnW2OM/eqrr+zkyZNtQkKCHTRokJ02bZqNiYmxW7ZssRdeeKGNiYmx3bp1s0uXLrXWWrth\nwwZ7zjnn2OjoaJuWlmb/9Kc/BbVbXl5u58yZY0844QQbERFhExMT7fDhw+17771XWWfq1Kk2JibG\n5ubm2vPPP99GR0fb1NRUe/fddwe1lZeXZ40xdtGiRfbBBx+0vXr1sqGhofazzz6z1lq7a9cue801\n19jk5GQbERFhBw4caJ999tla21i8eLFNS0uzkZGRdsSIEfaLL75o0nPYUgoLC23/of2ta4rLMhvL\nHCyzsa4rXbb/0P5Nv69aqO38/Hx7ww032L59+9rIyEibmJhoJ02aZPPy8qrV/fLLL+3IkSNtZGSk\n7d69u507d6794x//aF0ul83Pz6+s9/rrr9sLL7zQpqam2vDwcNurVy97zz33WLfbHdTeiBEj7IAB\nA+yGDRvsiBEjbFRUlO3du7d95ZVXrLXWvv/++3bIkCE2MjLS9u3bN+h+bEsKCwvtef3725Uul/U4\nb3rrAbvS5bLn9W/6a99SbVtrbVFRkb3llltsenq6DQ8Pt126dLHnnXee/c9//lNZ55FHHrGZmZk2\nMjLSDhkyxP7f//2fHTFihB05cmRQW1u3brUXXXSRjY6Otl26dLEzZsyw77zzjjXG2A8++KDefXrm\nmWesMcZ++OGHdvr06bZz5842Pj7eXnfddba8vNzu37/fXnnllTYhIcEmJCTYO++8s1obJSUl9rbb\nbrM9evSw4eHhtm/fvnbhwoXV6hlj7PTp0+2yZctsv379bGRkpB02bJj9/PPPrbXWPv7447Z37942\nIiLCnn322UH3uc+aNWvs6NGjbVxcnI2KirIjRoywH330UVAd38/ujRs32qlTp9r4+HgbFxdnr776\nanvo0KGg/rhcLmuMqXxcffXV1lrnZ3B6enq18/vabu7ram2FhYW2/1lnWdeCBZZVqyz//Kdl1Srr\nWrDA9j/rrCbf+y3dflt4b9XnbyIRaeMOHrT2q6+sfestax9+2NrbbrP2xz+2duBAazt2tN4PG86j\nY0drTz7Z2X/bbdY+8ohzXE6O004r8f0sAgbZFohDKQNNRKQFzZy5kJyc2/B4xgSUGjyeMeTkWGbN\nWsSSJXPaXNvgn+9k0qRJ9OnTh/nz52Ot5eOPP8btdnPBBRcwYsQIfv/73/Piiy8yffp0oqOjmTlz\nJlOmTOGSSy7h8ccfZ+rUqZx++umkpaUBMHv2bO677z6uvfZaTj31VAoLC1m7di3r169n1KhRlef2\neDyMGTOGYcOG8fvf/563336b2bNn43a7mTMn+Lr++Mc/cvjwYa677jrCw8Pp1KkTpaWljBgxgtzc\nXKZPn056ejrLli1j2rRpHDhwgOnTpwe18eyzz1JcXMwvfvELSktLWbJkCaNGjeLzzz+nc+fOjX4e\nW8LMe2aS0zsHT++ANHUDnl4ecmwOs+bOYsmCJW2u7U8//ZQ1a9YwefJkunfvTl5eHo8++igjR47k\nq6++IiIiAoCdO3dy9tln4/F4+M1vfkNUVBRPPvlk5f5AzzzzDLGxsdx+++3ExMSwatUqfvvb31JU\nVMSCBQv8l2AMe/fuZfz48Vx22WVceumlPPbYY0yePJkXXniBW2+9lRtvvJErrriC+++/n0mTJvH9\n998THR3dqGttKQtnzuS2nJygeT4MMMbjwebksGjWLOYsadzr05JtA1x33XX89a9/Zfr06WRlZbFn\nzx5f/uHFAAAgAElEQVQ+/PBDcnJyOPnkk3nssceYPn06I0aM4LbbbiMvL4+LL76YhIQEevToUdlO\naWkp55xzDlu3buWWW24hJSWF559/nlWrVjV6WNz06dNJSUnh7rvvZs2aNTz11FPEx8fz73//m7S0\nNObPn8/f/vY3Fi5cyIABA5gyZUrlsePHj+eDDz7gZz/7GQMHDuSdd97hjjvuYNu2bSxatCjoPP/6\n179YsWIFN910E+AMoRw3bhx33nknjz32GDfddBP79u1jwYIFXHPNNbz33nuVx65atYqxY8dyyimn\nMGfOHFwuF08//TTnnHMOH374Iaeccgrg/9l96aWXkpmZyX333cf69ev5wx/+QHJyMvPnzwfghRde\n4H/+538YMmQI1157LQC9evWqbKOm57K28qZcV1swc/58ci68EM9pp/kLjcFz2mnkALPuu48l8+a1\n2fbb8ntLRNoQtxt++KH2YZaBmVthYc58YxkZMGwYXH558DDLhIQGDbNsab5ROa+8srJlT9QSUbmj\n8UAZaCLSyurz39b09FEB2WFVHx6bmnquXbfONuqRklJ32+np5zbp+ubMmWONMXbKlClB5dOmTbMu\nl8suWLCgsmz//v02KirKhoSE2GXLllWWf/PNN9YYY3/3u99Vlp188sl2/PjxdZ7bd45bb701qHzc\nuHE2IiLC7tmzx1rrzx6Lj4+vLPN58MEHrcvlsi+99FJlWUVFhT399NNtx44dbXFxcVAb0dHRdvv2\n7ZV1P/nkE2uMsbfffnudfW0N6T9K92eHVX3MxqYOTLXrtq1r1CMlO6XOttMHVc9Kqa/S0tJqZR9/\n/LE1xtgXXnihsuzWW2+1LpfLrl27trKsoKDAxsfHV8tAq6nN66+/3sbExNiysrLKsrPPPtu6XC77\n8ssvV5b57s/Q0FD76aefVpa/++671hhTLVuxLRiVnl6ZHVb14QF7bmpq436grFtnR6Wk1N12DRlJ\nDREfH2+nT59e476ysjKblJRkhw4dGpQ9+Nxzz1ljTFCWjO+9vXz58sqyQ4cO2RNOOMG6XK5GZaCN\nHTs2qPz000+3LpfL3nTTTZVlbrfb9ujRI6gvr732mjXG2Pnz5wcdP2nSJBsSEmJzc3Mry4wxNjIy\n0m7ZsqWy7Mknn7TGGJuammpLSkoqy3/zm99Uu9f79OlTrZ+lpaU2MzPTjh49urLM97P75z//eVDd\nn/zkJ7Zz585BZTExMZVZZ4GmTZtmMzIyqpXPmTPHulyuoLKmXldbkH766f7MsKqPVats6rBhdl1h\nYaMfKUOH1tl++hlnNKn/beG9pQw0kTbA47G2oMDaTz6x9uWXrb3vPmuvvdba886ztndva8PCgn+/\np6ZaO3y4tVdeae1vf2vtM89Y+8EH1m7ZYm1FRWtfTb0Fj8pZqww0EZH2yFpLeXk0Tg5HTQzbtkUx\neLCto06trQN1t11eHoW1TZuo2BjDddddV+O+//mf/6ncjouLo2/fvmzatImJEydWlvfp04f4+Hhy\nc3Mry+Lj4/nyyy/ZuHEjvXv3rvP8vmwGn1/84he89dZbvPfee1x66aWV5RMnTqRTp05BdVeuXEnX\nrl257LLLKstCQkK4+eabufzyy/nggw8YO3Zs5b4f//jHdO3atfL7U089lSFDhlRmnbQV1lrKQ8rr\neunZVrqNwU8MbtxtdZg62y53lTf6vgoPD6/crqiooLCwkMzMTOLj41m/fj1XXHEF4Lx2Q4cOZfDg\nwZX1ExMTueKKK3jsscdqbbO4uJjDhw8zfPhwnnzySb7++msGDBhQuT8mJibovvHdn927d6/M3gEY\nMmQIQNB92xZYa4kuL6/r5SFq2zbs4MEt8BMFosob/9qD897/+OOP2b59OykpKUH71q5dy549e1iw\nYAEul6uy/PLLL+fWW28Nqrty5UpSUlL4yU9+UlkWERHBtddey69+9asG98sYwzXXXBNUNmTIENas\nWRNU7nK5OOWUU1i/fn1QX0JDQ6tltN5+++288sorrFy5khtvvLGy/Nxzzw3K+PHdaxMnTgyaXzLw\nHuzZsyf//e9/+e6777jrrrvYs2dPZT1rLaNGjeKFF16odk1Vf3afeeaZvPbaaxQXFxMTE1O/J6ee\nGntdbYG1lvLw8NozKYxhmzEMXru2cdkW1oLLVWf75R06HJPvLRFpAQcP+lezrCmLrKjIXzcuzp8x\ndvHF/jnIMjOdSfxryOxvj4JH5aw/Yv2mUABNRKSFGGMICyvB+Wha0x/FlpSUEt58szF/MBvGjSth\n+/ba2w4LK2mWIRcZGRnVyiIiIkhMTAwqi4uLo3v37tXqxsXFsW/fvsrv7777bi6++GL69OnDSSed\nxJgxY7jyyiuDAh3gfFjNzMwMKuvTpw8AeXl5QeXp6enVzpufn88JJ5xQrTwrKwtrLfn5+UHlNQXz\n+vTpw7Jly6qVtyZjDGHusLpuK1LCU3jzujcb1f64V8ex3W6vte0wd1ij76vS0lLuvfdennnmGX74\n4QdfRjnGGA4ELF+en5/P0KFDqx3ft2/famVfffUVM2fO5J///CeFhYWV5VXbBGq9PwM/+AN07NgR\nIOi+bQuMMZSEhdX10lOSkoJ5s+GvvQFKxo3Dbt9ee9thjX/tAe6//36mTZtGjx49GDx4MGPHjuWq\nq64iIyOD/Px8jDGVQwh9QkJCqr2/8/Pza3y/1nR/1FfVYE5cXBxAtXuj6s+z/Px8UlNTqw31zcrK\nqtwfqKb2oPq9GRcXh7W28lzfffcdAFdddVWN/Xe5XBw4cKCyvZquKSEhAXDu6+YOoDX2utoCYwxh\nhw87ga6a7m9rSfF4eDMgyN5Q4zwettfRftjhw8fse0tEGsjthq1ba5+sf8cOf90OHfzDLM84A6ZM\nqT7M8hjj8ThrGezaBbt3O19ffPEjPJ45R+X8CqCJiLSg8ePPYOnSd6rMU+Zwud5m0qThDBrUuLYn\nTqy77QkThjeu4SoiIyOrlYXUsnJObeW+QAk4WRCbNm3i9ddf59133+V///d/Wbx4MU888US1LJCm\n9PFYNv7c8SzNXYqnV/Wlul2bXEwaM4lBKY27sSaOnlhn2xPOm9CodsHJIHz22WeZMWMGQ4cOJS4u\nDmMMP/3pT/E0YtnxAwcOcNZZZxEfH8/cuXPJzMwkIiKCdevW8etf/7pam025b9uKM8aP552lS4Pm\nKfN52+Vi+KRJNPaHyhkTJ9bd9oTGv/bgzKd41lln8eqrr/Luu++ycOFCFixYwKuvvtqkdptDQ+6N\nptwXjb0HfffyokWLGDhwYI11qwbFmnJf1xbMcbvdNZa39/fW+BEjWPrpp8FzlHm5Pv2USeecw6Am\nrJQ5ceTIOtufcPbZjW4b2vZ7S0SqsBb27PEHxaoGyrZsgYoKp64xkJrqBMROOAHOPz94NcvUVCfD\ntR2zFvbvDw6I1fW1oMCJMQa0QN059M1LATQRkRY0b94vWbXqEnJyrDfQZQCLy/U2WVmLmTt3eZts\nu6XFx8czdepUpk6dysGDBznzzDOZM2dOUADN4/GQm5sb9N/wb775Bqg546yqtLQ0Pv/882rlOTk5\nlfsD+TI8An377bf1OtfRNu+ueaw6fxU5NscJdDkvPa5NLrI2ZjH30bltsu3ly5czbdo07r///sqy\nw4cPs3///qB6aWlpNb4eX3/9ddD377//Pvv27eP111/njDPOqCzftGlTo/vY1v1y3jwuWbUK653s\n3/vy8LbLxeKsLJbPbfzr05Jt+yQnJ3P99ddz/fXXU1BQwI9+9CPmzZvHggULsNayceNGRowYUVnf\n7XaTl5cXFDRKS0vjyy+/rNZ21fvjaEhLS+Mf//gHJSUlQVlotf2caSxf9lBsbCznnHNOs7QJtQfK\nEhISqr0voXr277Fi3v/7f6waN44cwHPqqc6HVmtxffopWW+9xdxGZHUezfbh2HtvibRrJSV1D7Ms\nLvbXTUjwB8QuuaT6MMuAqSraA2uhsLD+AbHdu/3xQp+QEEhKgi5doHNn52v//sHf+78aBg0qIS+v\nMVPiNJwCaCIiLSg2NpbVq5cza9YiVqx4gPLyKMLCDjJhwhnMnbuc2Cb8R7sl225Je/fuDZqvLCoq\nit69e7N169ZqdR955BEefPDBoO87dOhQuVpnXcaOHcvf//53Xn75ZX76058CzgeGhx9+mNjY2KAP\nEgCvvfYa27ZtIzU1FYBPPvmEjz/+mNtuu61R19mSYmNjWf3uambNncWKN1ZQ7ionzBPGhHMnMPfR\nuU2/r1qo7ZCQkGpZYQ899FC1rJaxY8eyZMkS1q5dWzk32e7du/nTn/5UrT1rbVCbZWVlPProo43u\nY1sXGxvL8tWrWTRrFg+sWEFUeTkHw8I4Y8IEls9t+mvfUm17PB6Ki4srh8cCJCUlkZqayuHDhzn1\n1FNJTEzkqaee4uqrr66cq+mFF16oNtzP995evnw5l1xyCQAHDx7kqaeeanT/Gmvs2LE8+eSTPPLI\nI0FzRC1evBiXy8UFF1zQLOcZPHgwvXr1YuHChUyePLnakNGCggKSkpIa3G50dHSNgbJevXpx4MAB\nvvjiC0466SQAtm/fzmuvvda4C2jjYmNjWf3mm8y67z5W3HUX5R06EFZWxoQRI5j75ptN/n3aku0f\nq+8tkTatoqLuYZY7d/rrhof7h1meeSZcdVXwMMv4+Fa7jPqw1on3NSQgVlYW3IbLBYmJwYGvvn1r\nC4g5McWGJNbVNeKnuSmAJiLSwmJjY1myZA5LltDkSf2PZtstpV+/fpx99tkMHjyYTp068emnn/LK\nK69w8803B9ULDw/n7bffZtq0aZWT+a9cuZKZM2dWm3+tJtdeey1PPPEE06ZNY+3ataSnp7Ns2TJW\nr17NkiVLqn0A7d27N8OHD+eGG26gtLSUJUuW0LlzZ+64445mvf7mEhsby5IFS1jCkpa5r1qg7XHj\nxvH888/TsWNH+vXrx+rVq/nHP/5R7YP/nXfeyfPPP8/o0aO55ZZbiIqK4qmnniI9PZ0NGzZU1jv9\n9NNJSEjgqquuqrx/XnjhhXbxPmiK2NhY5ixZAkta5rVvibaLioro3r07EydOZODAgcTExPD3v/+d\ntWvX8sADDxAaGsqcOXO4+eabGTlyJJdeeil5eXk8/fTT9O7dO6gfP//5z3nkkUe48sorWbt2LSkp\nKTz//PPV3tP11ZThhOPHj2fkyJHMnDmTzZs3M3DgQN555x3eeOMNZsyYUeMcko1hjOEPf/gDY8eO\npX///lx99dV069aNH374gX/+85/ExcXx+uuvN7jdwYMH895777F48WJSU1PJyMjgtNNO47LLLuNX\nv/oVF198MTfffDMlJSU8/vjj9O3bN2gRhWNJbGwsS+bNYwkt8/u0pdpvy+8tkXbLWicSVFNwzDfM\n0vfPP2OgWzcnINa3L4wZEzzMMiWlzQ2zLCmpXzDM97W0tHobiYnBQa9evWoPiHXq5GSVtZTgUTld\nWu5EKIAmInJUteQH+6MZNKjtXDWVG2OCym+55RZWrFjB3//+dw4fPkxaWhr33nsvv/zlL4OOCw0N\n5e233+b666/nzjvvdD7Yz5nDXXfdVWf7PhEREXzwwQf8+te/5rnnnqOwsJC+ffvyzDPPcOWVV1ar\nf9VVV+FyuXjwwQfZtWsXQ4YM4eGHHyY5Oblez0lrai/31UMPPURoaCh/+tOfKC0tZfjw4bz33nuM\nHj066Dxdu3bl/fffZ/r06SxYsIDExERuuOEGunbtys9+9rPKep06deKtt97i9ttv56677iIhIYEr\nr7ySc845h9GjR9frWmq7f2orb2vay2sfFRXFTTfdxLvvvsurr76Kx+Ohd+/ePPbYY1x77bWAf9Xd\nRYsWcccddzBgwABWrFjBLbfcQkTASmGRkZGsWrWK6dOn88gjjxAVFcWUKVMYM2YMY8Y0/L/PDb3O\nwPrGGN544w1++9vf8vLLL/PMM8+Qnp7OwoULmTFjRrXjGnoPBhoxYgSrV6/mnnvuYenSpRQXF9O1\na1eGDBlS62rJR/LAAw9w3XXXcdddd3Ho0CGmTp3KaaedRqdOnXjttde47bbb+NWvfkVGRgb33Xcf\n3377bbUAWlOvqy1q6T4eL+8tkTatpKT2IZabNzv7fTp18gfEJk0KHmbZs2erD7M8dKhhAbGDB6u3\nkZAQHPQ65ZSag2FdujjBs9A2FEkKHJWzbNlKtm9vuXOZtjaJZ30ZYwYB69atW8egxs7ALSLSBOvX\nr2fw4MHo51Dzu/rqq1m+fHnQqootJT8/n4yMDBYuXNgmh2uKHM+stXTu3JlLLrmEJ554orW7I3LM\naO73lv4mkjanvBy+/772LLLdu/11IyL8wywDs8d82wErHB8Nhw83LCAWOKWaT1xc7QGwql+TkiAs\n7KheYovx/SwCBltrmz1lug3FDUVERETkeHX48GHCq/wX/9lnn2Xv3r2MHDmylXol0v7pvSXHJGud\nCFJtWWTffx88zLJ7dycglpUFY8cGB8q6dm3RYZZlZc7qkUcKhvm2a/r/cUxM9Un1R46sHgzzBcTa\n2doD7YYCaCIiIiLS6tasWcOMGTOYNGkSiYmJrFu3jj/+8Y9kZ2czceLEBrVVWlrKgQMH6qzTqVMn\nwo6Vf7mL1KE531siR1Vxcd3DLAPHIiYm+gNip51WfZhlhw7N1q2KivoFxHxfa1irhaio6pPqn3lm\nzVlinTtDZGSzdV+aQAE0ERFpk472nG7tYV4ekWNZeno6PXv25OGHH65crXfatGnMnz+f0AZOtvLy\nyy9z9dVX17rfGMM///lPzjrrrKZ2W6TNa873lkizKi93JuSvbZhlQYG/bkSEPyg2ciRcc01wFlnA\nSrQN5XbDnj31D4jt3Vu9jYiI4MBXr14wbFjtATGtzdG8ioqKmHnPTF5Z8UqLnkc/MUVEpM15+umn\nefrpp4/KudLS0nD7UvxFpNWkpaXx2muvNUtbY8aM4b333quzzsCBA5vlXCJtXXO+t0QaxFrYubPu\nYZYej1PX5fIPs+zfH8aNqz7Msp7/7PR4YN+++g2X3LXLCZ5VnRq+Q4fgoFdamjOxfm1zicXE1Lt7\n0syKiooYdv4wcnrn4BnhgW9a7lwKoImIiIjIMSU5ObldrKArIu2TtVaZ6z6Fhf7AWNVA2ebNzhKR\nPklJ/oDY0KHBwyx79Kh1mKW1zjDI+k6qX1Dgn/7MJzQ0OODVrRucfHLtAbGOHRUQay9m3jPTCZ71\n9sC2lj2XAmgiIiIiIiIidfANEXvjvTcoDyknzB3G+HPHM++uecTGxrZ291pOWZl/mGVNWWR79vjr\nRkb6A2KjRlVf1dL7PFnrxN0qA1+fw+5VtQfEdu925h0L5HL5h0P6Js/v37/2gFh8fPsKiFlr8VgP\nbuvG7XFT4amo3K6trMJTEbS/prLmOKZB7bRUuwFl+5fvx15pj/ykNgMF0ERERERERERqETREbIIH\nDGBhae5SVp2/itXvrm6/QTRrYceO2ifq37o1eJhljx5OUCw7Gy66CDIzsekZlHTJYJdJZtduUxn8\n2rnTsusDNztfcbO7wM2ugkIK9rgp2OumvMINrgpwucG4weUmIdFNQmIFnRLdxKe46dvfzakJFcTH\nu+nofcTGuYntWEFktBtL7UGXAo+bndaNe78b974mBG8aGOBprqCQ27bu9CIGQ4grhBATQqgrtHI7\nxOX93rtd0/76HBPiCiE8NLz6MQ1sN8SEcO+f7qXQ1LB0aQtQAE1ERERERESkFkFDxHwMeHp5yLE5\n3HLXLcyaPavNZgp1KD5Ep50HSNxRSOLOIrrsLKLzzhK67C4heddBwsv917UvNoxtSR34ITGc/L5h\n5A9JJbdjCLlxIeTHuDiEhwp3LhWeb3GXuPFscB6Y4GCY89UDcTiP3kd+nvd5H7lVd+z3PhrIZVxN\nCvAcKaDTIaQDkaGRNR/ThHYbc0xzB7ra0xDlx0Mep9AWOoHtFqYAmohIE+Xk5LR2F0RERERazbH4\nt5Db42bj3o18tvMznnvzOTyXemqs5+nl4ennn+bphJZZ/Kg+QZUIj4se+zyk77P03FtBjz0VdN9T\nQfc9ZXQrKCOuxD/+8VAHF9s6RfF9QhQ5PTqzpV8Mm2NiyY2IZWN4R/ZWRFB6MBR3eQjYECgJgaJQ\n2BpCeFgIUZEhJESGEhMVQnRUCLHRIcTEhBAXE0rH2BA6xoQQ1zGEuNhQOoS2XIDnSO24jKtdBYGk\n8cafO56luUvx9Kr5PdqcFEATEWmkpKQkoqKimDJlSmt3RURERKRVRUVFkZSU1NrdaJS9h/ayYecG\nNuzcwGc7PmPDrg18sesLSitKwYLLumrPbjGQGJfIn6f8mdCQ0GbNOHIZl3MOj6faMEv3d7lUfLcZ\nk7+ZsJ1bMN5lJN0mhL3RPdkWnsFGVwZ/65DJlxUZ5BzOIJdMdpd1hh0GdjgT5QfOFTY6Grqk1jyH\nWFJSrXP8i7SqeXfNY9X5q8ixOXiiWjaIpgCaiEgj9ezZk5ycHAoKClq7KyIibYbbDaWlcPiw89W3\nfehQzeVVt+tbr7S0/n1yuSAiwv8IDw/+vrby+tSrut2YhIdx465n+/bHqPkTuiUl5QbefPPxhjfc\nCNY6r2F5uTNpd+AjsKym7SPtP1JbDWm/prbKy/1TNTUHY5yV+8LCnK+B2zWVBT4ackxN++uqV99+\nHO3km6SkJHr27Hl0T9pAFZ4Kvin4pjJYtmGXEzD7oegHAMJDwunfpT/ZydlcftLlDOw6kAFdBnDa\nitPIs3n+t6glaDuWWM7tdW6T+la+ez/7/vMdB7/YTNnXTqAs7IfNxOzMJf5AHmHuw5V1d5ku5NoM\nNpNBLsPZjLO9MyqTw116kJgcGjS5/oDOMKpKQKxzZ+dnlkh7Fxsby+p3VzNr7iyWrVjGdra32LkU\nQBMRaYKePXu2+T8WRUSsdRZSO3jQeRw61PjtI9UrK6t/vyIiICrKWbgtKip4u2NHSE72l9dWrz7b\nYWEt99w2h4kTL2Dp0t14PGOq7XO5VjJp0lgGDRrUCj1rfzweJ5hWVuYE1MrL/ds1lTV1/5GOOXiw\nYW3aZlxILjTUyRgKC3Mevu2aypq6v7nbDAlp+vXvLtntZJTt/KwyYPbl7i8pczs/pLp37M7A5IFc\nNfAqspOzyU7Opk9iH0Jd1T8ijx4xmie+eoK4fEj8FhLcsC8E9vSBAz1hzNnV37sVFVBQ4F9Ncs+2\nw5R+k4/NzSX0+81E7dxM/N5cOhdvplvZZhLsPrp4jy0mms1k8K0rk13RY9iXmkFxl0zKu2dAejrx\n3WP8WWMBQbHIyKY/byLtUWxsLEsWLGHqT6cyePDgFjtPowJoxpibgF8CXYHPgOnW2k9rqXsGsAA4\nEYgC8oEnrLUPBtSZCjxNcCy/1Fob1Zj+iYiISN2stZobpA1wu52gU0sGtHzb9f1g7nIdOWiVkBBc\n3pjgVkSEcy6BefN+yapVl5CTY71BNGeJP5frbbKyFjN37vLW7mK74XI5AZj2OtTMl/3XkkG/+uwv\nKWn48eXlzfc8GFP/oFtIhzIq4r/mUMcNlMRuoDj6MwojN1AaugOAUBtJJ/dJdPEM4ixzNamubFJD\nBxBf1omwHRC2Bw5shDVhsL6Wc37/bSQpb4fxlKecsVQuwslbn8C1n4ax6oRIJuV7cG/dTtSOXDru\n2Uznks1kkksGm+lLLqlsw4Xzg7iCEHZHprE3LoOiPoPY0G0iNi2DsD4ZRPbPpFOfJDK7GAZEN99z\nKiJN1+AAmjHmp8Ai4FrgE2AG8I4xpo+1tqZxTCXAw8AG7/Zw4EljTLG19g8B9Q4AfQhOjBUREZFm\nUlRUxMyZC3njjY8oL48mLKyE8ePPYN68XxIbG9va3WszrPVnjrR0ttbhw0fuj094eN0Bqbg4SEmp\nXt6YbC3FVo+u2NhYVq9ezqxZi1ix4gHKy6MICzvIhAlnMHfucr0/jyMhIc4jIqK1e9Jw1tae/dcc\nQb3DZZb95TvZ5v6MHWxgt9lAQcgG9ofl4DFO9C7qcDoxJdl03fczIg9k02HfQEIO9KKiPISyMthc\nDt/U0r7bXfu1xfEKL+LmwoAyA4wDnrLlvPvtQ/x+46N08Ph/qB/smExpSgaenhmEnHAWZf0yCM/K\nxGRmENq9OymhoaS0yCshIi3F2AbmCRtj1gAfW2tv8X5vgO+Bh6y199ezjeVAsbV2qvf7qcBia22n\nBvRjELBu3bp1SmkXERE5gqKiIoYNu4ScnNvweEbjz3B5h6ysB1i9uu1/SPd4mp6tVd/gVn3nUTKm\naVlYDcnWao4hTdI+KENUjnelFaXk7M6pNgRz98HdAESHRTMgeQADkwdWDr8c0GUAcRFxjT6nxwPl\npW7KN2/FszEXNm2C3FxMXi4XvvUXPsDWMkshDDOhrF68ENOrF2Q4wyyJVvqYyNG2fv163xDOwdba\n9c3dfoMy0IwxYcBg4F5fmbXWGmPeA4bVs40feevOrLIrxhiTB7iA9cBvrLVfNaR/IiIiUrOZMxd6\ng2eB87QYPJ4x5ORYZs1axJIlcxrVdnNlax0puNWQSeM7dKg7IBUb659fqynBrQ4dlK0lzU/BMzle\nWGv5oegH/6T+3oDZNwXf4LZOSlivhF5kJ2dz46k3VgbMMhIy/CtUNlRxsbOaZa4/SEZuLq5NmwjP\nyyPcNxbVGOjeHZuZSbgxlatcVmUA6wqBm2/WLwSRY1xDh3AmASHAzirlO4G+dR1ojPke6Ow9fo61\n9umA3d8A1+AM84wD7gD+bYzpZ63d1sA+ioiISBVvvPERHs+cGvd5PGN4/vkHSExsXLZWXcNeAhlz\n5IBUly5Nz9aKjFS2lohIW3Ow/CBf7f6Kz3Z8VrkC5oadG9h7aC8AHcM7kp2czdlpZ3PzaTeTnZzN\nSV1OIja8gdnRHg/s2OEPjgUEydi0yZnR3ycqCjIzoVcvGDfOv52Z6WSRhYdjgIKO8diiA7VmoEVk\nglkAACAASURBVJVFRSjwLXIcOJqrcA4HYoChwAJjzEZr7csA1to1wBpfRWPMaiAHuA6YXVejM2bM\nIC4uOFV38uTJTJ48uXl7LyIi0saVl8PWrbB5M+Tl+R+5uZYtW6Khxj/9AQz790fx6KOW6GhTLSAV\nHe2s7tXUbK3wcP1zXkTkWGetZcuBLUEZZRt2buC7vd/hsR4MhhMSTyA7OZsZQ2dUDsFMi0urfxDq\n0CHnF1zVINmmTc4vwcCU5ZQUJyB2wgkwerQ/QJaZ6aQi1+OcYy6/jLeeeIJxNex7E7jgCn32FDna\nXnrpJV566aWgsgMHDrToORs0B5p3COdB4BJr7YqA8meAOGvtj+vZzkxgirU2q446fwHKrbVX1LJf\nc6CJiMhxpaICfvjB+cxQNUi2ebMTPAucuys11fkHekYGvPnmuRw48HdqDqJZ0tPPY/Pm947CVYiI\nyLGiuKyYL3Z9ETQEc8PODRw47HyIjY+ID5qnbGDyQPp36U9UWFTdDVsLu3dXzx7zfd0WMEgpPNz5\nRReYPeb7mpHh/AeniYqKivjxkCHc+vXXXGitfxVOY3jwxBN59eOP2/w8oiLHgzY1B5q1ttwYsw4Y\nBayAykUERgEPNaCpECC8tp3GGBcwAHirIf0TERFpz9xu5zNBYFAscPv774OHS3bt6g+QnX66s+37\nvkeP4FXcbr75DJYufafKHGgOl+ttJkwY3pKXJiIi7ZjHesjbn+dklO34rHL45aa9m7BYXMZF38S+\nDOw6kAt6X1AZMOvesXvtWWVlZZCfX3OQLDfXmavMp3Nnf2BsxIjgIFlqKrgaOR9aPcXGxvLqxx+z\naNYslqxYQVR5OQfDwjhjwgRenTtXwTOR40RjVuG8FHgGuB74BJgBTAROtNbuNsbMB1IDVti8EdgC\nfO1tYgTwAPCgtXa2t85dOEM4NwLxwJ3ABJyooe+4qv1QBpqIiLQrHg9s315zgCwvD7ZscYZh+nTp\nEhwU822np0NamjM0sr78q3DO8AbRfKtwvk1W1uJ2sQqniIi0vMLDhXy+8/OgIZif7/qc4jInoJUY\nmcjArgPJ7pLtfE3OJispi8iwGn4p7d1b8zxkubnOf4V8adOhoc4vt8DAmG87IwM6djx6T0A9aKVc\nkbapTWWgAVhr/2KMSQLuBpKB/wKjrbW7vVW6Aj0CDnEB84F0oALYBNxhrX0yoE4C8KT32H3AOmBY\nbcEzERGRtshaZ97iqkMrfdv5+c4/3H2SkvwBsR/9KDhIlpbmzD3WXGJjY1m9ejmzZi1ixYoHKC+P\nIizsIBMmnMHcuQqeiYgcb9weN7n7civnKPM9Nu/fDECoK5QTk05kYPJALj7x4sohmF1juvqDRxUV\nTiDsX/+uedL+/fv9J4yP9wfHhgwJDpJ17+4E0doJBc9Ejk8NzkBrK5SBJiIiR5tvSpaqQysDA2SB\n8xZ36hScNVY1QNaaMSv991xE5Pix79A+Pt/1edAQzC92fcHB8oMAJEcnB81Tlp2czYlJJxIeGg6F\nhTXPQ5ab6/ziq6hwTuJyOfMHVJ2HzPc1IaEVnwEROR60uQw0ERGRY5W1sGdPzRP0+7YPHfLXj4vz\nB8UuuCA4SJaW5uxvqxQ8ExE59lR4Kvhuz3f+jLJdTsDs+8LvAegQ0oF+nfuRnZzNpf0udYJmnU8i\n+UCFP0j2r28gd6U/SFZQ4D9BTIw/IHbxxcFBsp49oUOHVrpyEZGWpwCaiIgcN6yFfftqX8UyLw9K\nSvz1Y2KcgFhGBpx3XvUssvj4o38NIiIiAHsO7qmco8wXMPty95eUVjip0KmxqWQnZ3P5gMv5Uce+\nDDoUT8Y+S2jeFvjHJshdBZuecn4BBs4v0K2bExDr1w/Gjw8eapmUBPoHjIgcpxRAExGRY8r+/bVP\n0r95MxQV+etGR/uDYiNHVh9qmZCgzwkiItK6yt3lfLPnm6BJ/Tfs3MC2om0AhIeEc1Ln/gwPP4E7\nkk5nQEk0aXs9RP93u3e45bPOBJ0+ERH+gNiYMcGT9mdkBC/hLCIilRRAExGRdqWwsO4A2YED/rqR\nkf6g2PDhMGVKcIAsMVEBMhERaTt2lexy5ijzDr/csHMDX+3+ijJ3GR0qYFhFV8529+DGQyfRZ/9A\nUgtKidm6C5P7NRwMmO4nOdkfJBs1KjhIlpKiX34iIo2gAJqIiLQpxcW1zz+Wlwd79/rrhof7A2RD\nh8JllwVnkXXurM8IIiLS9hyuOMzXBV8HD8Hc8RkVu3eRuQ+yDoQxtDyZnxdFkbGvJ4k7Cgnfvhtj\ndwA7ICzM+UWXmQkjzoarrwkOkjXnMs4iIgIogCYiIkfZwYN1B8gC5yru0MGZjD89HQYPhokTgwNk\nXbo4i36JiIi0RdZathdv989R9sN/2fX1WuymTaTv8ZC5D84tjuLGA2GkFpQSUTkPZzkkHoLMFDgh\nE8ZUWdGyWzcICWnNSxMROe4ogCYiIs3q0CHYsiU4MBa4vWuXv25oqD9ANnAgXHRR8CT9KSkKkImI\nSPtQWlHKV7u/Iue71ezY8G+Kv9mA2ZRL8q6D9NoLk/YbbjtgCfU49W1ICJ6ePQjp1RuG9QqerD8z\ns20v5SwichxSAE1ERBrk8OHqAbLAIFngPMUhIc6q9unp0L8/XHhhcIAsNVX/QBcRkfbFVlSwPedT\ntvznffZ++Sll331Nh/wf6LyjkMx9MOiQv25pdDilPTMJPakP0ScOwPTyZ5KZHj0ICQtrvQsREZEG\nUQBNRESClJXB999XzxzzbW/b5q/rckH37k5QrG9fGD06eJL+bt2cLDMREZF2pbgYcnM5/G0OOzf8\nm+KvN0BuLrFbd5NccIhUN6QCHgO7OoVTmJqE55QBlJx4EtEDhhLRtz9kZhLRqRMRmoxTROSYoI81\nIiLHmfJy2Lq19lUsf/gBrHXqGuMEwdLT/Qt5BQbIund35jEWERFpVzweJ2V60ybIzcVu3EjJN597\ns8m2ErPPmYwsHEgMg/2dYFeXGEpP6U7oCX1J6DeY7j8aQepJw+gaEUHX1r0aERE5ChRAExE5xlRU\nOEGw2ibp//5753ODT2qqPyh21lnBk/T36OFM5C8iItLuHDrk/OLzBsl8X90bv8Ns3ozrcFll1R2x\nho0JltwE2D44AnfmiUT1GUCX7KH07Xcm/br0JzssqvWuRUREWp0CaCIi7Yzb7QyjrCtAVlHhr9+1\nqz8odvrp1QNkERFH/RJERESazlrYvdsfIAsIkrFpU9CcAxVhIezoHMm38R6+6HiQTSMhv5MLT0Y6\nCf0GkdVzMNnJ2YxKzqZbbDeMhl2KiEgVCqCJiLQxvlElta1iuWWLMwzTp0sXf1DstNP82+npzgqX\nkZFH/RJERESaR1kZ5OfXHCTLzXXmKvNVTYxnb0o8+YmhfDXIsGZYB3Jiy8hNgLLkeLJTTiY7OZvs\n5GymJQ8kq3MWEaH6L5KIiNSPAmgiIkeZtbBzZ+2rWObnO58XfJKS/AGxH/0oeBXLtDSIjj761yAi\nItJs9u6tHhjzbQfOOxAaik1P52CPrmzP6srGYZ35T3Qh/3Jt5cOw7RSH7yfUVUxWUhYDuw4lu0s2\nP/EGzLrGdFVWmYiINIkCaCIizcw3oqS2VSzz86G01F8/IcEfFBs/vnqALDa2FS5CRESkuVRUOIGw\nqkMsfcGy/fv9dRMSIDMTevXi0OBstnQK48uOh/i4w27+5c7lsz1fcqhiIwBdY7o6GWVdRnBF14Fk\nJ2dzYtKJdAjR5J0iItL8FEATEWkga2HPntpXsczLc+Yt9omL8wfFLrggeBXLtDRnv8jRZq1VNoaI\nNJ/CwprnIcvNdf5z5Juc0+WCnj2dINngwTBpEu6MdPITQ/hP5AHWleby2c7P2LDz32wt3Apl0GFf\nB/p37k9212wmDZxcOQyzS3SXVr1kERE5viiAJiJShbWwb1/N84/5tktK/PVjYpyAWEYGnHde8CT9\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bhissjJDiYkaPGMHMyZO1v6WK0qrBVR10blXXxVVYi26tUO8C95MFVVFBQbQODa1ViFXd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\n1jXS8OzTT+G88yA3F1aurJfh2YHiYu7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bY/wl5/mDwMchhM7Ae0A6icMKrtiO+kqV55+H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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f801419ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'loss'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-c27daa722396>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_test_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'X_test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0my_val_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'X_val'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Validation set accuracy: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_val_pred\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y_val'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test set accuracy: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_test_pred\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y_test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'loss'"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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 },
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